1,000,000 CVs wanted

Are you trying to write the best CV to impress recruiters and get your dream job? Do you know that, on average, each corporate job offer attracts 250 resumes and you will only have a 2% chance to be interviewed for your dream job? Yes, 2%. Now you may wonder how recruiters pick the 2%. Well, most of them use talent-management software to screen CVs, weeding out up to 50% of resumes, which have never been looked at. Ouch, yep, this might include yours and that is why you always receive a standard rejection-but-thank-you email afterwards.

At JANZZ.technology, we are building an alternative solution which allows each resume to be evaluated by artificial intelligence and, most importantly, each applicant will receive feedback from the system elaborating on their missing skills (why you were not hired) and possible suggestions for further education (how you can improve your chances) in order to secure a similar job in the future.

For this purpose, we are asking you to help us improve our machine learning algorithm. Here is how you can contribute to creating the human element in AI systems:

  • Send your CV to info@janzz.technology. If it makes you feel more comfortable, you can delete your personal information.
  • Language: we are looking for resumes in French, Italian, English, German, Greek, Norwegian, Dutch, Portuguese, other languages used in the EU, Korean, Chinese, Japanese, Thai, Indonesian, Malay, Vietnamese and Arabic.
  • Format: Any. From the standard 2 pages word doc. to the most creative and innovative ones.
  • We promise not to spam you or use your CV for any other purpose other than machine training. We will also delete your CV after it has served its purpose.

Please help us to share the message and we will keep you updated with the latest number of resumes we received.

ParaEmpleo identified as a best practice in the field of AI in Latin America and the Caribbean

The Inter-American Development Bank (IDB) plays a key role in driving the development of artificial intelligence (AI) in Latin America and the Caribbean (LAC) as a tool to address social challenges. Working together with regional experts, the IDB designed the fAIr LAC initiative to promote responsible adoption of AI, thereby improving the delivery of government services such as Public Employment Services (PES) and creating development opportunities in the region. The fAIr LAC initiative aims to close gaps and reduce the growing social inequality in LAC.

As one of the first steps of this initiative, the IDB recently conducted a preliminary analysis of the progress made by countries in LAC regarding the use of AI in social services. The report Artificial Intelligence for Social Good in Latin America and the Caribbean: The Regional Landscape and 12 Country Snapshots introduced the best practices in the field of AI in the region. JANZZ.technology is delighted to share that its project ParaEmpleo – a job matching solution realized in collaboration with Paraguay’s Ministry of Labor, Employment and Social Security – is one of them. JANZZ.technology is keen to continue contributing to projects that use AI for social good and adopt ethical and responsible principles, thus generating better social services in more regions.

Click here to download the full report.

JANZZ.jobs, a white-label solution with anonymized application procedures

Reports [1],[2] have shown that an anonymized procedure at the very beginning of the application process can largely reduce bias and significantly improve equal opportunities. Here at JANZZ.technology, we have been building the architecture of our solutions around the anonymized process since 2010. JANZZ.jobs is a white-label solution designed to avoid bias during the first steps of the application process by concealing users’ personal data. Two profiles are created to separate personal information and job-related information. The job-related profile contains all information that is relevant to the matching process such as occupation, skills, soft skills, education, experience, availability, salary and so on. This profile is accessible from the start. The personal profile containing information such as name, gender, nationality, date of birth, marital status, portrait picture etc. is only shared upon user approval and is not used for job matching.

In addition to this anonymized procedure, JANZZ.jobs’ unique semantic matching engine, powered by JANZZ’ key technologies, searches and matches jobs and candidates based on the similarity of terms, i.e. synonyms and other relations – as opposed to mere keyword comparison. It also compares the degree of skills, identifies cryptic occupational terms through context, performs gap analyses and more. (To learn about semantic matching please check out our previous article JANZZ.technology – providing semantic technologies powered by ontology)

This white-label solution is currently serving several Public Employment Services (PES) across the globe. Our customers choose JANZZ.jobs because

  • the platform is scalable with state-of-the-art modular components which can meet the varied requirements of PES of all sizes,
  • the process to establish such a platform is fast, easy and cost-effective, and it is especially an ideal solution for PES that need to build from scratch,
  • the solution has been tested and built with years of experience from many other customer PES around the world, and it has proved to be stable, reliable and efficient,
  • the SaaS solution JANZZ.jobs saves PES from running a designated IT department. Instead, they can trust the professional team at JANZZ to manage the databases and can automatically benefit from the JANZZ.technology updates and upgrades.
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User story: How a country in Central America built its job-search system in no time

Due to adverse economic environment our customer, a country in Central America, is struggling with increasing unemployment rates – especially among the youth. We collaborated on a local project designed to strengthen the country’s private Technical Vocational Education and Training (TVET) system and to equip the local youth with the skills they need to successfully enter the labor market. Our mission was to create a modern platform that pools all the talent and work opportunities of the country and successfully matches people and jobs.

In just 90 days, the JANZZ.jobs platform was implemented as a white-label product and is now operated as a SaaS solution to help our customer

  • implement a job-matching platform from scratch in a fast and cost-effective way,
  • boost its economy and strengthen its society by providing higher visibility and better matching for the country’s talents and jobs,
  • assist local educational institutions investigating and comparing labor market demand with graduate profiles to better align their curricula with market needs,
  • provide access to information on local TVET centers, training and scholarships, tips for interviews and CVs etc. to improve users’ chances in the labor market,
  • introduce an anonymized application matching system to improve equal opportunities for its citizens when applying for jobs.

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Like PES, many small to medium sized recruiting agencies are faced with tight resources and the need for an automated solution to perform superior candidate search and matching in order to focus more on a small pool of candidates qualified for the vacancies. Therefore, we also receive requests from recruiting agencies. These agencies are the leading recruiting firms in specific branches, specialized in C-level positions for global clients. With JANZZ’s innovative technology and fully customizable solutions JANZZ.jobs can be tailored to the specific needs of small to medium sized recruiting agencies and help them take their business to the next level. They will benefit from

  • being fast to market by launching a modern and powerful platform to boost the business,
  • fully personalized brand experience with business logos, colors and email templates,
  • traffic-based cost generator to maximize revenue,
  • real-time job matching in over 40 languages, and much more.

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User Story: Recruiting agency in healthcare, medtech and pharmaceuticals looks for solutions to better support its clients

One of our customers is a recruiting agency specialized in the life science and healthcare sector in Europe. It consists of a small team with less than 10 people. Typically, the talent acquisition managers spend 50% of their already limited time on searching and analyzing CVs. They feel they could deliver more suitable candidates to their clients by reallocating some of this time to conducting in-depth interviews with a smaller number of qualified candidates. For this reason, our customer was interested in a recruiting platform to locate appropriate skilled personnel quickly and efficiently.

By using JANZZ.jobs, our customer has sustainably reduced the processing time of their acquisition managers. Meanwhile, thanks to the anonymized procedure, which largely protects the privacy of their clients, the quality of suitable applications has increased through

  • significantly more precise, multilingual skills matching between applicants and vacancies in comparison with traditional job platforms,
  • increased transparency for all applicants as to why an application has been rejected,
  • improved skills-based active sourcing; vacancies are only visible and open to contact for suitable candidates.

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To learn more about JANZZ’s anonymized procedures and building your branded hiring solutions, please contact sales@janzz.technology

 

 

[1] Ines Böschen, Dr.Ramona Alt, Annabelle Krause, Dr. Ulf Rinne and Prof. Dr. Klaus F. Zimmermann. 2012. Pilot project ‘Depersonalised application procedures’. URL: https://www.antidiskriminierungsstelle.de/SharedDocs/Downloads/DE/publikationen/AnonymBewerbung/Kurzfassung-Abschlussbericht-anonym-kurz_englisch.pdf?__blob=publicationFile&v=4

[2] Eva Heinimann and Ralf Margreiter. 2008. Anonyme Bewerbung: Ein Zürcher Pilotprojekt für mehr Chancengleichheit und innovative Lehrlingsselektion. URL: https://www.panorama.ch/pdf/bba4814b.pdf

Free movement of skilled workers in the EU and beyond are more important than ever

Do you know how to build a house? If not, maybe you should consider learning it right now. In 2030 there is a high chance that we will have a critical shortage of construction workers. This is your opportunity to pick up the skills such as installing electrical wiring, carpentry, blockwork, waterproofing and plumbing.

According to the 2020 report by the Schweizerischer Baumeisterverband (Swiss Building Association)[1], the Swiss construction industry is facing massive problems: fewer and fewer young people are willing to learn a trade in construction, and many of the current skilled workers are about to retire. Compared to 2010, 40% less young people started a bricklayer apprenticeship in 2019. This will have a critical impact on the entire industry because the majority of construction foremen, supervisors, and construction managers are recruited from the bricklayer pool. On the other hand, the proportion of people aged over 50 in the main construction industry is up to 36%. The expected outcome of this combination is alarming.

In recent years, the level of professional qualifications on construction sites has risen sharply due to digitalization, leaving the typical unskilled worker now barely in demand and creating a skill gap between supply and demand of workforce in this sector. However, the drivers of skills shortages are much more complex.

Demographic trends

Lower birth rates and increasing life expectancy are the two key demographic trends in Switzerland and the European Union. An ageing population paired with a shrinking working-age population have intensified the situation in the labor market. According to data from Eurostat [2], the share of working-age population in the total EU population decreased by more than 2% from 2010 to 2018 and the median age increased by almost three years to 43.1 during the same period.

On top of this, the impact of baby boomers heading into retirement has yet to fully unfold. A study by Credit Suisse [3] states that a total number of around 1.1m people will reach retirement age in the next ten years and the younger generation will not be able to fill the vast number of highly skilled jobs vacated by this post-war baby boom generation. Studies from other Central European countries reveal similar expectancies concerning the impact of baby boomers on their labor markets.

However, multiple reports have indicated a shortage of skilled workers in the construction sector for countries that do not have this demographic shape, such as South Africa and India. Apparently, a more general factor is causing the skill shortage in construction on an international level.

Negative image

Many studies from around the globe show that the construction industry has a negative image, especially among the youth. For instance, in a study conducted in a high school by the national business employment weekly in South Africa, careers in construction came in at number 247 out of 250 potentially attractive career options [4]. CITB data in 2013 [5] found that in the UK, the overall appeal of the construction industry as a career option had fallen to only 4.2 out of 10 among adolescents aged 14 to 19, and the Union of Construction, Allied Trades and Technicians (UCATT) also reported a 14.6% fall in construction apprenticeship numbers in 2013. This was confirmed again in a 2017 poll. In the US, only 3% of youth aged 18-25 wanted to work in the construction trades in a 2017 survey [6].

Despite the bright prospects of a career in an in-demand field, the youth just can’t seem to get past the industry’s bad image: low wages and job security, health and safety concerns, low-quality work, and tough working conditions are the aspects that the youth most associate with jobs in construction and deter them from pursuing a career in this sector. Moreover, the rise of digitalization feeds a false fear that jobs in construction will fall victim to automation in the future.

Indeed, some occupations in construction have a higher potential for automation, but technically, it is not feasible to perform certain tasks with robots. Even physical work, which many assume most likely to be automated, can be too challenging for robots – especially physical tasks in unpredictable and changing environments.

Moreover, when referring to professions in construction, people mostly think only of on-site activities, often overlooking the professions related to sub-sectors such as real estate, procurement, architectural or engineering activities, which enjoy a higher social status and could be very attractive to both genders.

 Growing interest in the academic route

In the dual education system practiced most notably in Germany, Austria, and Switzerland (DACH), most career paths in the construction sector start with vocational training and apprenticeship. A long-standing tradition in these countries, the dual system was well received by parents, adolescents, and society at large. Lately, an increasing interest in university degrees has posed a direct challenge for the professions in the construction sector and for other skilled trades.

By the time university graduates are faced with making a career choice, it is far too late to attract them to the construction site. Research shows that timing is key for sparking interest in construction professions, and the highest success rates are reported when engaging school children at the primary level. Accomplishing this will require joint efforts to bring about fundamental changes to the education system and foster deeper collaboration between industry and educational institutions.

Indispensable free movement of skilled workers

This is certainly a promising strategy for the long term and future workforce generations. In the short and midterm, however, national economies – particularly in industrialized and industrializing countries – will highly depend on flows of skilled workers among countries all over the world to alleviate the workforce shortages. Indeed, as noted by the ILO [7], migrants are a vital source of skills and labor for industries such as construction. By nature, construction requires flexible teams with a variety of skills, and is thus often deeply dependent upon migrant workers due to their mobility and flexibility. Therefore, solutions contributing to the free movement of persons will rapidly grow in demand. However, to ensure true free movement of workers, an important issue needs to be addressed from the start: the recognition of qualifications among different countries and even regions.

Challenges in skills recognition

In the EU, the Professional Qualifications Directive (PQD), the European Qualifications Framework (EQF), and the European Credit System for Vocational Education and Training (ECVET) serve as the main instruments for the recognition of professional and academic qualifications to support workers’ mobility across Europe. However, despite significant efforts such as the launch of the European Skills, Competences, Qualifications and Occupations (ESCO) classification system, skills recognition and matching among the member countries are still very disappointing, not to mention internationally. Comparing skills across borders poses two significant challenges: first, many countries have their own classification systems or taxonomies, which each need to be mapped into a transnational system. But not all have already effectuated this. Second, not all skills and qualifications translate easily. For instance, a carpenter who completed an 18-month apprenticeship in the UK will have a different skill set than a carpenter with a three-year apprenticeship in Austria, even though both completed standardized training for the same skilled trade.

For this reason, more and more investments and expectations are placed on ontologies and semantic technologies as the solution to overcome these challenges. Currently, JANZZ’s ontology is the largest multilingual encyclopedic knowledge representation in the field of occupation data. Not only can it translate the many linguistic variations in occupational jargon into a common vocabulary, but more powerfully, by human expert curating, it can truly compare differences and similarities in education and qualifications across borders and language divides – thus delivering a meaningful contribution towards true free movement of skilled workers.

Contact us now to understand how JANZZ’s ontology can assist you in education and qualification recognition for your workforce.

 

 

[1] Zahlen und Fakten 2020, Schweizerischer Baumeisterverband SBV

[2] European Commission, Improving the human capital basis, Analytical Report (March 2020)

[3] Credit Suisse, Fear of recession exaggerated (September 2019)

[4] Makhene, D., Thwala, W.: Skilled labour shortages in construction contractors: a literature review

[5] UK Construction: An Economic Analysis of the Sector (July 2013)

[6] Young Adults & the Construction Trades, NAHB Economics and Housing Policy Group

[7] Buckley et al.: Migrant work and employment in the construction sector, Geneva: ILO, 2016

Perhaps, this time, “working from anywhere” is here to stay

When people were still debating the future of flexible working and how technology and digitalization are going to change how we work, after COVID-19, that future has arrived much sooner than anticipated. Gartner stated in its report that 48% of employees will likely work remotely compared to 30% before the pandemic. [1]

How has the workplace changed over time?

Back in the days, the workplace was much less tech-oriented, and business conversations were conducted through landlines or in person. Documents and files were all hard copies. Employees were expected to work independently, and cubicles were present in the office. Since the 1950’s, major changes took place and the emergence of teamwork, computers, the internet, and business-oriented software has had a tremendous impact on how we work.

Over the past 20 years, one of the most significant workplace changes has been the technical transformation to a digital workplace. The workplace has undergone even greater revolutions such as telecommuting, zoom, co-working space, and flexible working. The elimination of fixed workplaces first appeared in Silicon Valley technology companies known as flexible working or the upgrade of activity-based working. Basically, employees want more freedom during work and their homes or even a café are gradually becoming their second “office”.

Remote work, who is ready or, more precisely, who is capable?

In an article published by Harvard Business Review, the robustness of digital services, internet infrastructure, and digital payment options were used to evaluate 42 significant global economies in terms of the readiness for remote work. [2] In the survey we can see that countries such as Singapore, UK, US, Netherlands, Norway, Canada, and Germany are placed in a better position while countries like India, Indonesia, Thailand, Chile, Philippines, and China are shown less prepared. However, the readiness of technology is only the external factor triggering the practice of remote work, and it can be influenced with the effort of country leaders by investing in infrastructure and technology.

The internal factor, thus the structure of different industries within a country is the key here. In countries where the main economic activities fall into the agriculture and manufacturing sectors, it is unlikely for its workforce to work remotely because farmers and blue-collar workers simply cannot work from home. On the other hand, countries whose service sectors produce a majority of their economic value, have a workforce, or more precisely a knowledge workforce, that is not attached to the workplace anymore. According to the knowledge economic index (KEI) from the World Bank Institute, countries such as Denmark, Sweden, Finland, Netherlands, Norway, Canada, and Switzerland, are among the top. [3]

The downsides of remote work

COVID-19 forced companies around the globe to practice full-time flexible working and some countries, where flexible working was not common, needed to adapt to the new situation quickly. For such countries, it is also important to understand the negative sides of flexible working when handled unproperly.

When the time spent in the office is no longer the key factor for being paid, flexible working must have a new pay-related appraisal criterion, hence performance is taken as the new measurement. In order to justify their efficiency and be seen as high-achieving and trustworthy, employees often agree to higher target agreements. This can lead to so-called “interested self-endangerment” causing the detriment of health. Therefore, companies should not ask too much of their employees or else they might risk the utility of flexible working. [4]

Digitalization shifts the boundaries between free time and work allowing people to spread their work across the whole day so they can better combine work and life. However, in a survey conducted in Germany, half of those surveyed said that digitalization increases the intensity of their work. They feel more stressed and their workload has piled up, and they also feel subjected to stricter supervision. [4]

The challenge in the gig economy era

Flexibility in jobs is no doubt one of the key features in the gig economy which is transforming our labor market drastically. The work today is changing towards being more cognitively complex, technology-dependent, collaborative, mobile, and border-crossing. This presents a huge challenge for governments and corporations today to match the right skills and qualified people with jobs and to identify the education and learning gaps to meet future business needs.

For almost a decade, JANZZ.technology has been following the trends shaping the future of work and working with many labor markets worldwide. We are active in semantic parsing, searching, and matching within occupation domains. Our technology can realize data-driven HR decision-making, speed up people analytics, and use workforce data for business performance prediction. To find out more about what we can do please contact sales@janzz.technology

 

 

 

[1] Gartner. 2020. 9 Future of Work Trends Post-COVID-19. URL: https://www.gartner.com/smarterwithgartner/9-future-of-work-trends-post-covid-19/

[2] Bhaskar Chakravorti and Ravi Shankar Chaturvedi. 2020. Which Countries Were (And Weren’t) Ready for Remote Work? URL: https://hbr.org/2020/04/which-countries-were-and-werent-ready-for-remote-work

[3] Wikipedia. Knowledge Economic Index. URL: https://en.wikipedia.org/wiki/Knowledge_Economic_Index

[4] UZH Magazin. 2018. Interview: “Working flat out”. URL: https://www.magazin.uzh.ch/en/issues/magazin-18-4/gesundarbeiten.html

Workers age 50+: ready for the scrap heap or worth their weight in gold?

Systematic discrimination against workers age 50+ in candidate selection – or why this is not the issue in the vast majority of cases.

Read on, the following article will not just repeat the same arguments as always on this particularly important topic, which are usually based on assumptions and politically deadlocked positions. We will provide you with new, statistically relevant and number-based arguments that allow you to take a different view on the challenges of older workers and, to the same extent, on our education policy. But let us start at the beginning.

The statistics bring it home…

Suppose we want to fill a new position. We already have 80 suitable applications and CVs, including ones from young professionals, experienced professionals and applicants age 50+. Let the selection process begin. We sort by relevant skills and competences, professional experience, education and training, language skills, specializations, industry knowledge and so on. We reduce first to five, then to three candidates, who we invite to an interview. Importantly, we hide all personal data during selection, or rather, we make a non-discriminatory first selection using XAI.

At the end of this not so fictitious example and after many long, personal interviews and assessments, we choose a 27-year-old, multilingual university graduate with almost three years of experience in the right industry and the best matching scores in the areas of hard skills/competences and soft skills, communication skills, appearance, etc. A surprising choice? Hardly. It is rather the logical result of a structured, transparent and above all fair selection procedure. Remember, the hiring HR professionals were not aware of age, gender or salary expectations for the first steps of the selection. It would have been more of a surprise if one of the 50+ candidates had won the race – for statistical reasons alone: in the total of 80 applications there were only seven more or less suitable 50+ candidates, i.e., less than 10%.

Imagine that a 54-year-old, less qualified candidate had been chosen, contrary to the robust findings of the structured selection process and results of the interviews, primarily because of his age. This would have been just as discriminatory as an inherent preference of male candidates or favoring the candidate with the necessary connections.

Let us explain in more detail why this choice is logical and fair, and why other, similar selection processes usually have just as little to do with age discrimination or with the argument that companies avoid recruiting 50+ candidates for financial reasons.

There are always better qualified candidates out there. No matter how good yours are.

Well-trained, enthusiastic and experienced engineer, 50 plus, seeking – this is a scenario that has become bitter reality for many older workers in recent years. In our example, by the way, six of the 80 applicants were ahead of the 50+ candidate, having even better qualifications, mostly just recently acquired or refreshed, and higher degrees. There was only one criterion, ‘relevant experience’, on which he came in fourth, just narrowly missing the interviews for the final selection round. In short, the candidate was not unsuitable or rejected just because he was over 50, there were simply better-suited and objectively better qualified candidates for the position.

By the way, according to some of the more serious statistics, jobseekers in many industries in Switzerland already start facing more difficulties when they reach their mid-40s. At this age, the chances of finding a suitable job fall significantly in more and more cases. Despite an exceedingly positive economic environment and stable labor market with an exceptionally low unemployment rate before Covid-19, even highly qualified older workers were concerned about potential, longer-lasting unemployment. The fact is that once the older generation have lost their job, it is difficult for them to find a new, equivalent position. This is primarily because, usually for the first time in many years, they will have to face up to the ever increasing, ever better educated, multilingual competition and keep pace with younger, highly motivated and equally ambitious applicants.

To be very clear at this point: The sad exceptions do exist, companies with a real prevailing ‘anti-50+ policy’. Such a policy makes no sense at all, economically or otherwise, but there have always been companies that could neither calculate nor had a reasonable and fair personnel strategy. However, the real reasons why people in their 50s are increasingly faced with unemployment are complex and found on both the employer and employee side.

Last relevant vocational training: commercial apprenticeship 1981

The current labor market is becoming more and more specialized and is exposed to ever-faster technological change in many sectors, not only because of advancing digitalization. There are several reliable surveys that consistently show that, by the age of 30, more than 60% of knowledge acquired up to that point is already outdated or no longer relevant for professional progress.

In recent years, digital technologies, channels and thus evolved processes have come to the fore, rendering tasks more demanding and complex, especially for older workers. For example, compare the top 20 required skills from 2008 and 2018, say, on LinkedIn or in similar surveys. The ongoing transformations and the digitalization of competitive skills are quite dramatic.

This is just one of the reasons why training is being invested in everywhere and more than ever. That is a good thing, we all fought for this privilege for a long time and have repeatedly stressed the importance of good, modern education for every economy. Access to education as affordable as possible for all. A whole variety of tailormade educational models, dual education, vocational baccalaureate, semester abroad, MBA, CAS and much more. Comparing the manifold possibilities of today’s educational landscape, not only in Switzerland, with the options that were available at the time of our 50-year-old engineer, there have been huge, mostly positive developments – consistently and in all areas and aspects that are key to a successful professional life. Moreover, ambitious young professionals will gladly pay, or rather invest, 60000 US dollars and a few months of their lives for an MBA or tens of thousands of francs for challenging advanced training, certifications or postgraduate studies in order to have a better chance in the competition, which is becoming tougher for younger workers as well. We must all be continuously willing to make such investments, including the time commitment and renunciation of family life and leisure time ensued. Lifelong learning and continuous training are more than just buzzwords.

To keep up with a constantly evolving labor market, it is absolutely necessary to continuously train and extend our skills and competences, on average every 5 to 7 years. Work experience is certainly valuable, but that value is diminishing in more and more areas because the businesses they are based on are often outdated after a few years or have disappeared completely from the market. The ever-accelerating cycles of innovation in basic processes, tools and market and production mechanisms render the by far largest asset of experienced workers increasingly obsolete in comparison with younger, often better trained co-applicants.

The problem for the 50+ generation is that their good education was completed many years ago. Their knowledge, should they need to transition from a familiar and well-known environment to a new field of work, is thus no longer up to date.

Also, many 50+ applicants list only a few, if any, current training courses on their CV. For instance, a TOEFL test from 1993 may be the last entry under ‘languages and communication’, hidden among an abundance of in-house courses and trainings with lavish certificates of little meaning or relevance to a new position. This can be confirmed statistically by parsing and carefully evaluating large quantities (several million) of anonymized CVs: on average, the last relevant qualified formal training was completed 11.2 years earlier for 50+ candidates in Switzerland. In cases of successful professional reorientation or re-entry into a profession, it was several years less. As a reminder, the iPhone as the first actual ‘smart phone’ was launched almost exactly 11 years ago. Several other, significant digital processes and tools have followed since – at ever-shorter intervals.

In such cases, companies cannot be blamed for not considering a 50+ applicant for the simple reason that younger applicants are statistically in the majority and are moreover better qualified or have more up-to-date competency profiles. It would therefore be particularly important for older jobseekers to continuously adapt their strengths and qualities to technological change (whether we like it, want it or not…) and to consider continuous, targeted further training or even reorientation. Self-commitment is called for and this is not the responsibility of employers.

Know-how and relevant competence profiles beat experience.

Another reason why older applicants’ dossiers often end up on the rejection pile is the number of years of service. Applicants who have worked in the same department, company and industry for 20 years have specific work experience but often lose touch with the rapidly changing professional world outside the company. However, this long-term, one-dimensional experience is not the main obstacle in itself: it is often more likely the fact that the applicants’ profile is strongly tailored to their former employer and their qualifications are too limited or they are too specialized, having spent many years in the same function with similar tasks. As a result, flexibility and new professional opportunities are often deemed difficult. A new employer would need to invest in thorough onboarding and possibly in retraining. Of course, this may be necessary for a younger applicant as well. However, it may greatly devalue the importance of acquired professional experience in the competition with other candidates. Even if relevant work experience is still very important in general, its importance has diminished in a fast-moving and even faster changing economy. Ten years of experience are no longer twice as good and meaningful as five. Or rather, only if the competence profile has been developed in parallel with experience gained and according to the latest requirements. Unfortunately, this is exceedingly rare, as the data from the many parsed CVs clearly show.

Protect a lack of qualifications?

Lately, there have been repeated discussions about special protection against termination or special quotas for over 50s, in the hope that this constantly growing problem will be mitigated in the long term. But are these ideas not extremely unfair and discriminatory towards younger and usually better qualified workers? Workers who are already severely disadvantaged when it comes to major topics such as retirement provision, and thus are already proving more than enough solidarity with older workers.

Such an approach leads to unacceptable discrimination against younger generations by protecting less-qualified applicants. Not only that, such a rule would also mean that current 50+ jobseekers may no longer be recruited because employers fear that they will not be able to dismiss them. This type of reaction can be widely observed in countries with rigid employee protection laws such as Germany and France, where, as a result, many employers strongly favor fixed-term over permanent contracts. Special protection against termination is thus not a solution, it is a fallacy.

Another idea aimed primarily at mitigating the consequences of systematic age discrimination is the bridging pension (rente-pont). But if that is not the driving factor behind long-term unemployment of older workers, then this will just amount to yet another instance of discrimination against younger jobseekers. Instead, older jobseekers should be trained – many of them barely know how to apply for an opening. Looking at their CVs, you immediately encounter the showcase syndrome: instead of listing relevant skills, the document is adorned with information of no relevance whatsoever such as obsolete programming languages learnt 20 years ago. As a consequence, such an applicant will often seem desperate and insecure, not like a proud, promising new employee who will support the department and enhance it.

So why hire over 50s at all?

Too expensive, too little professional expertise, too inflexible – these are classic stereotypes older employees are branded with. True, the younger generation is usually more flexible and mobile in terms of time and place of work. Sell my beloved house after twenty years and move far away to another city or canton? No thanks. The reality that wages automatically rise with increasing work experience and age is another fact that is never questioned and rarely discussed publicly. And yet, this is another point where performance should be assessed rather than age. Why not earn the most when we are at our most performant and our expertise is its most comprehensive and up to date?

And finally, young jobseekers often also boast more extensive language skills and are, for the most part, much more IT-savvy. However, a few arguments speak in favor of the older generation: they have a high sense of duty and responsibility, very often have a positive attitude to work and are usually regarded as balanced and notably more consistent.

Anyone who now thinks that these issues can be reduced with anonymized AI-based application procedures is completely wrong unfortunately. These procedures do not focus on the person, but on relevant skills, current education and training, language skills, industry knowledge and specializations. An evaluation of various applicant selection processes in a wide range of occupational groups and industries has shown that, in fact, (with the exception of select management positions) the pool for the next round usually contains a significantly smaller proportion of 50+ candidates than in conventional selection procedures. This in turn proves that it cannot be due to the age of the candidates because all personal characteristics such as age, gender, origin, etc., were fully disregarded in the selection process and thus played absolutely no role in the matching and ranking, which formed the basis for the interview invitations.

We must therefore find other strategies. The key is ‘to be found’ instead of ‘to search’. Positions tailored to over 50s are often not found in job postings. However, there are technological tools that ignore these common prejudices against older employees. Machines decide on the basis of matching data points. They do not know discrimination against age, gender, ethnicity, etc. Older applicants should utilize this opportunity, especially to find out what they can draw on to increase their chances. These tools also give very objective and sober answers to many questions such as how many matches do I really get with my current qualifications? Where are my personal skill gaps? In the mind of the machine, there is no ‘I didn’t get the job, wasn’t even shortlisted just because I’m over 50. Sure, that figures…’

Using these tools, employment services, recruiting companies, job portals and others can make attractive employment proposals to 50+ talents, but also pinpoint individual placement challenges. For a gap analysis, feel free to ask for help at info@janzz.technology

Welcoming Jimena Renée Luna as our new VP of Customer Integration, Emerging Markets

We are proud to announce that Jimena Renée Luna will be joining JANZZ.technology as our new VP of Customer Integration, Emerging Markets. She will be responsible for all accounts in LATAM, EMEA and Southeast Asia.

Jimena is well-established and highly experienced in advising client governments and international organizations on tech policy, job creation, and economic development. Throughout her career, she has worked 10+ years designing and implementing related projects with teams across Latin America, Europe and Africa. At the World Bank, she performed research on labor markets and launched innovative solutions for job creation. In addition, she has worked for the U.S. CIO at the White House on digital policies to improve how citizens and businesses interact with government – helping to close the gap between the public and private sector on technology and innovation. More recently, she has worked on projects in Africa to promote the digital economy and digital development.

Jimena is enthusiastic about the job matching products and digital solutions offered by Swiss-based JANZZ.technology to clients around the world. She is confident that digital platforms, big data, and AI will drive the economy of the future. At a time when the world is facing a digital transformation and changes to the labor market, she is excited by the opportunity to work directly with global clients to provide them with digital solutions for job creation.

Jimena will be joining us on May 15. She will start working from Washington, DC, and then transfer to our headquarters in Zurich at a later date. We look forward to seeing Jimena applying her experience, enthusiasm and professionalism to our mission to better serve our clients.

Feel free to reach out to Jimena via email at j.luna@janzz.technology . She is fluent in English, French, and Spanish and will be happy to answer any questions you might have.

JANZZ.technology – providing semantic technologies powered by ontology

If we ask a computer to translate the English sentence “the box is in the pen” into other languages, it will most likely interpret the word “pen” as the object we use to write with, this being the more frequently used meaning. But then the sentence will be nonsensical because, as we know, a larger object cannot be inside a smaller one.

Language processing or natural language processing is a much bigger challenge in AI than, for instance, image processing. We humans realize that, for this sentence to make sense, the word “pen” must mean a small area surrounded by a fence. A computer, on the other hand, lacks contextual knowledge and thus the logical reasoning needed to translate the sentence correctly. Another example would be “John is flying to the Big Apple on Tuesday.” You can probably guess what the result would be.

This is where semantic technologies come in. Among the many available methods, semantic techniques aim to improve computers’ understanding in processing natural/conversational languages through knowledge representation. Semantic technology is powered by ontology: it relies on semantic information encoded in ontology to identify nodes (e.g., words) that are semantically related.

At JANZZ.technology, we offer superior semantic technologies including semantic extraction, searching and matching powered by our comprehensive ontology in the domain of occupation data. To illustrate, JANZZ.technology’s semantic solutions can realize the following smart applications:

– Job searching and matching on related concepts

Related concepts are not (necessarily) synonyms but concepts which share similarities, sometimes given in completely different words or even languages. For example, “Neonatology” and “Pediatrics” are related concepts. With the information stored in ontology, semantic technology can identify how closely these two terms/professions are related to each other and, importantly, what kind of training/certifications one of these professionals needs in order to perform the other one’s job. This can be extremely helpful when transforming workforce skills on a large scale such as public employment services.

As another example, “Creative Director” and “Web Designer” are also related concepts but to a much lower degree compared to “Neonatology” and “Pediatrics”. If you are looking for a “Web Designer”, our semantic technologies would also recommend someone with job title “Creative Director” combined with skills in CSS, HTML and UX, or suggest such skills. Of course, “Concepteur Web”, “Nettdesigner”, “مصمم على شبكة الإنترنت” or “网页设计师” will also be matched. Related concepts can also be skills or education. For example, if you are looking for someone experienced with ERP systems, our semantic technologies know that candidates whose CVs list SAP, JD Edwards and MS Dynamics are all good matches because these are all ERP systems.

– Job searching and matching on degrees of skills

Semantic technology is not only able to match job postings and CVs containing the same skills, but it can also compare the degree of skills. For instance, “MS office skills” is a broad term and listed in many CVs. If you are looking for a Spreadsheet pro, you don’t want to be matched with a myriad of CVs listing basic MS office or beginner’s level Excel skills.

Similarly, if you are searching for professional CAD software skills, our semantic technologies would match CVs with CATIA, OpenSCAD or Rhino rather than TinkerCAD or BlocksCAD because the different specificities of CAD software are also stored in our ontology. Moreover, our semantic techniques not only identify levels of skills, but also report any training necessary for candidates to transform skills from one CAD software to another.

– Concept identification through interpretation of the context

Semantic technologies help identify cryptic concepts through context. Job titles can be very challenging for computers to identify. In the sentence “Company X is looking for an RF System Engineer, Building 8, Menlo Park, CA,” our software is able to decode each part of the sentence with the information stored in our ontology, such as industry codes, company names and places of work. In this case, “Building 8” is not an address but instead a mysterious department for hardware development at Facebook, and the “RF System Engineer” refers to “Senior Radio Frequency Engineer”.

– Job matching on overall dimension of occupation data

Some job titles, such as dentist, pilot, carpenter and Android app developer, already contain a lot of information about the specific position. When matching these jobs, it is possible to match almost exclusively on job titles. However, other titles like teacher, consultant, assistant, engineer and coordinator are much less specific. In such cases, one needs to include other criteria such as industry, skills, education, experience, etc., in order to conduct an accurate and meaningful matching. Semantic solutions from JANZZ.technology can perform such tasks with the data linked in ontology.

– Identifying gaps in the information

In contrast to machine learning, which is proficient in pattern recognition and classification, an ontology models meaning. It helps a system to understand CVs and job postings and perform gap analyses, thus creating a more user-friendly experience. For example, when matching candidates and jobs, semantic technologies can recommend skills, education or training which a given candidate lacks and thus help candidates optimize their CV.

Are you a large international corporation, organization or public employment service? Do you want to have the right technology to prepare and accompany your labor force throughout the digital transformation? Do you want to improve user experience during the application process? Do you want to build a more powerful system which makes your products stand out from the HR tech crowd? To integrate the latest semantic extraction, searching and matching technologies powered by JANZZ’s ontology, please write now to  sales@janzz.technology and let JANZZ.technology assist you.

Is reskilling and upskilling the real cure for today’s skills shortage?

Digitalization, automation and AI pose a great threat to today’s job market that requires constantly changing skills. However, some of the skills are not missing due to the evolution of technology, but rather due to a loss of attractiveness. This is especially the case for positions with an unusually high number of vacancies or such that remain vacant for a long time.

 

According to the Swiss Skills Shortage Index, “a skills shortage exists if there are more vacancies than job seekers in an occupation.” Last year, the Adecco Group compared in its Swiss Job Market Index job advertisements with the number of job seekers registered by the Vacancies and Job Market Statistics Information System (AVAM), which yielded the 2019 Swiss Skills Shortage Ranking.

As in previous years, in 2019 engineering occupations such as structural and electronics engineers are most wanted by Swiss employers. They are followed by technical occupations, fiduciary and IT professions. The ranking further indicates that compared to 2016, when the measurement was conducted for the first time, the skills shortage in 2019 is 22% higher across Switzerland. [1]

There are many reasons for the skills shortage. The rapidly changing skills requirements caused by technological innovation are believed to have the most profound impact on the risks of skills mismatch and shortage. Similarly, Hay’s Global Skills Index 2019/20 reported the highest talent mismatch since the index’ launch in 2012 and they, too, believe that technological development is one of the main contributing factors [2].

On the part of businesses, many companies facing the threat of talent shortages, which might damage their commercial success, prepare themselves for new technologies by upskilling their existing workforce, investing in training, encouraging lifelong learning and raising the retirement age.

There is no doubt that continuous upskilling throughout a career will become the new normal, but is this really the key to overcoming skills shortage? If it were, how come that the situation looks as if things are going in the opposite direction?

Another report published by a Swiss online job portal and Zurich University of Applied Sciences (ZHAW) provides further insight into the Swiss job market. The report compared more than 100,000 job advertisements with the number of clicks on Swiss job portals and, thus, reveals people’s interests in specific jobs in a more direct fashion.

In the German-speaking part of Switzerland, professions in administration, HR, consulting, sales and customer services, marketing, communication, and executive boards received more interest (clicks) than the job advertisements posted. However, jobs in areas like production, telecommunications, construction or nursing received less interest (clicks) compared to the job advertisements posted. [3] This suggests that economic incentives as well as social recognition are becoming increasingly important for people when it comes to choosing a profession.

Last year, there were over 6000 professional care vacancies in Switzerland. This number has doubled compared to five years ago.[4] Reporting on the healthcare workforce supply and demand in Switzerland shows that care workers graduating in the near future will only cover 56% of the demand until 2025.[5]

In the case described above, the problem doesn’t have to do with up-or reskilling. It is rather about the ways in which more people – especially younger ones – can be encouraged to pursue a career in jobs that are considered less attractive. What is even worse, evidence shows that due to bad working conditions (e.g. little income, long working hours, too much stress) a large share of young people has switched their working field either right after their apprenticeship or after a mere few years of professional experience. This includes professions in childcare, hospitality, catering services and handcrafts.

Today everyone is talking about automation, digitalization, AI, upskilling and reskilling. We must remember that there are still many jobs that are unlikely to be automated but essential to our daily lives. And these jobs are losing in popularity. It is important for governments and education systems to take action on increasing awareness and to promote such professions. As written in the OECD Employment Outlook 2019, the “future of work is in our hands and will largely depend on the policy decisions countries make.”

For almost a decade, JANZZ.technology has been observing and working with many labor markets worldwide. Our latest product JANZZdashboard! creates transparent and easy to understand gap analyses of the labor market. This will give governments a clear idea of which skills are available and which ones should be expanded or redeveloped. To learn more about our solutions please write now to sales@janzz.technology

 

 

 

[1] Spring Professional. 2019. Swiss skills shortage index 2019. URL: https://www.swissinfo.ch/resource/blob/45398900/860c466e7be6e615ba922c24c9edf5ee/adecco-study-data.pdf [21.01.2020]

[2] Rachel Muller-Heyndyk. 2019. New technology causing skills gaps and stagnant wages. URL : https://hrmagazine.co.uk/article-details/new-technology-causing-skills-gaps-and-stagnant-wages [21.01.2020]

[3] Robert Mayer. 2019. Die meisten Stelleninserate, die geringste Nachfrage. URL : https://www.tagesanzeiger.ch/wirtschaft/in-diesen-berufen-herrscht-ein-mangel-an-fachkraeften/story/18953945 [21.01.2020]

[4] Albert Steck. 2019. Offene Stellen auf Höchststand. URL: http://jobs.nzz.ch/news/6/arbeitswelt/artikel/421/offene-stellen-auf-hochststand [21.01.2020]

[5] Veronica DeVore. 2016. When caring for patients gets competitive. URL : https://www.swissinfo.ch/eng/showing-off-skills_when-caring-for-patients-gets-competitive/42524090 [21.01.2020]

 

 

 

 

 

 

JANZZ ontology – empowering your data and realizing smart applications

Ontologies have been around in artificial intelligence (AI) research for the last 40 years.[1] Just as trends come and go, ontologies too have had their ups and downs. Introduced in the 80s, ontologies became popular in the mid-90s. After machine learning (ML) came on the scene in 2000, the widespread opinion was that in the future every task performed with a computer (by means of AI and ML) could be solved with a smart algorithm. A lot of companies invested heavily in these algorithms hoping to have the next breakthrough in AI.

With the rapid development of AI and ML, especially after the emergence of the convolutional neural networks (CNN), the core technology – deep learning (DL) – has been growing rampantly in terms of parameter size and computing complexity. Today some of the most complex models have reached a scale of billions of parameters. [2]

Still, concerns have been raised regarding the current mainstream DL. Let us take supervised learning in image recognition as an example: the images used to train the AI models need to be manually identified in terms of the position and contour of the target objects, for the models to be able to find implicit pattern features between the data after comparing different labeling results. If we recall how we learn things as infants, us human beings can easily identify and classify different objects without needing this kind of instruction. [2]

DL methods have made tremendous progress and are now able to extract knowledge from the training data. However, this knowledge is not explicitly explainable, because the so-called “black box” training cannot reveal the complex relations hidden within the models. When facing new problems, the current DL models are unable to apply their acquired knowledge to solve new challenges in an effective way. [2]

There is another big concern regarding big data and the privacy issue related to it. What is more, current DL methods are based on big data which is not applicable to industries that generate small amounts of data such as certain fields in medicine and human resources. This case requires AI systems to have the ability to reason and judge, which can only be successful in specific domain areas. [3]

Many powerful ontologies already exist for specific domains, examples include the Financial Industry Business Ontology (FIBO) as well as numerous ontologies for healthcare, geography or occupations. It is widely believed that knowledge integration and DL are the important ideas for further amplifying the effectiveness of DL. For this reason, ontologies have made it back into the spotlight, along with many equivalents such as knowledge graphs or knowledge representations.

At JANZZ.technology, we started to build our ontology – JANZZon! – in 2008, before the tech giant Google invented and popularized the term “knowledge graph”. JANZZ.technology has been building its ontology using domain experts with various backgrounds (e.g. intellectual property law, fluid dynamics, car repair, open-heart surgery, or educational and vocational systems).

Today, JANZZon! is the largest multilingual encyclopedic knowledge representation in the field of occupation data. The main focus lies on jobs, job classifications, hard and soft skills, training/qualifications, etc. The number of stored nodes and relations comes to more than 350 million!

Integrated with both data-driven and expert consultation taxonomies, JANZZon! covers ESCO, O*Net, ISCO-08, GB/T 6556-2015, DISCO II and the UK skills taxonomy from Nesta just to name a few. Currently, 9 languages (German, English, French, Italian, Spanish, Portuguese, Dutch, Arabic and Norwegian) fully cover occupation, skills, specializations, function, education, etc., and we are working on achieving the same level in a total of 40 languages in 2020.

Being the backbone of our job and skills matching technology, JANZZon! represents the knowledge at the deepest level where all the entities have been encoded and vectorized in the semantic space. Therefore, when searching and matching, our technology can truly understand a concept and its semantic meaning and thus guarantee meaningful results.

If you are wondering how ontologies can help you empower your data in the fields of human resources and labor markets and aid you in realizing smart applications? Please write to sales@janzz.technology

 

[1] ODSC. 2018. Where Ontologies End and Knowledge Graphs Begin. URL: https://medium.com/predict/where-ontologies-end-and-knowledge-graphs-begin-6fe0cdede1ed [2019.11.20]

[2] Li Jun. 2019. Shen Du Xue Xi: Xin Shi Dai De Lian Jin Shu. URL : https://www.ftchinese.com/story/001084827?page=1&archive [2019.11.20]

[3] Cai Fangfang. 2019. Qin Hua Zi Ran Yu Yan Chu Li Ke Xue Jia Sun Maosong: Shen Du Xue Xi Peng Pi Zhi Hou, Wo Men Hai Neng Zuo Shen Me? URL: https://www.infoq.cn/article/OvhfhpPChTLpsMgrf43N [2019.11.20]