High Potential Startup #11: Workera.ai
Personalized Data-AI Skills Transformation
At time of publication: Series A | Total Funds raised 21mn USD
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In the past decade there has been an exponential explosion in all kinds of new data generated. And there have been significant developments in the field of AI that can extract value from this data.
Data and AI are being used and will be used in multiple applications across sectors: medical diagnosis, assessing risk in multiple types of insurance, predicting demand more accurately, better crop management in agriculture, accelerating legal research, voice search in local languages, hunting for rare minerals, assessing real estate prices, monitoring financial crime, accelerating drug discovery and so on.
There is a huge requirement of talent who can collect, analyze and interpret data, model this data and apply AI techniques to get value out of this data.
Below is a sample of jobs for ‘Machine Learning’ in the United States that turns up hundreds of thousands of results.
The World Economic Forum forecasts a massive increase in demand from employers for Data and AI talent. Around 97 million new jobs may be created between 2020 and 2025. These jobs will also help in net job creation (i.e. create more jobs than they displace).
Turns out there is a huge shortage of talent with these skills and hence a massive market opportunity to help skill, train or upskill talent to meet this demand from employers.
Considering these jobs provide above average salaries, workers will also be keen to seize this opportunity. Universities also have begun to offer courses in these areas. But this will barely meet the demand. And universities restrict opportunities to many segments of the population and those in real need of these jobs are often left out.
Outside of universities, anyone motivated to learn a new skill encounters typical questions in their ‘skilling journey’.
The right guidance, assessment and feedback can help talent in finding the right skills to go after, understand current skill level and abilities and saves time by focusing on gaps rather than having to spend time in full courses.
Workera.ai is an Data-AI skills assessment (and guidance) startup that helps talent:
understand the pathways to a career in AI career
understand where they currently stand, the skill gaps and the progress they need to make
get a credential that helps prove skill levels to employers
Workera has co-created assessments with leading software industry corporate partners and basis conversations with more than hundred leaders in machine learning, data, AI roles. It offers assessment in six common job roles in Data/AI field: Data Scientist, Machine Learning Engineer, Data Analyst, Software Engineer-ML, Machine Learning Researcher, Software Engineer.
Each role spans a few broad tasks/skills one needs to master or be familiar with and Workera tests skills basis preferred or recommended role.
The Workera assessment assigns three levels of proficiency in the various skills:
(1) Beginning, (2) Developing and (3) Accomplished.
An effective Data Scientist as per Workera will require ‘Accomplished’ proficiency in Data Science, Machine Learning, Business Acumen and Mathematics and maybe lower proficiency in other broad skills.
Workera works for both new-to-the-field and those with experience in the field and recommends pathways for both sets of talent.
Based on recommended track/role, one can assess themselves in multiple skills required to gain proficiency.
The assessment (taken from home) is ‘adaptive’ which is useful for higher (and uniform) precision testing of skills. Workera claims to draw questions from a huge question bank covering more than 1300 micro-skills.
Once each section is completed, there is a proficiency level assigned.
Workera is not a training company but recommends a learning ‘playlist’ (free and paid) customized to the individual. Workera calls this ‘precision upskilling’ - focusing on learning basis your current skill level can help increase focus and optimize learning time as well.
Learners can retake the tests after some gap and get a chance to improve their scores.
Workera has released a beta version of a jobs marketplace that enables test takers to apply to companies with their scores (and a few other details).
Workera’s approach to this market gap in shortage of Data-AI skills is very impressive. While many startups would be tempted to attack this market opportunity by focusing on training/bootcamps/skilling of talent, the truth is that there is abundant information and training material freely available online.
What is truly lacking is a gold standard in assessment of Data-AI skills . From an employer perspective, it greatly helps if there is some benchmark in assessment of talent that it can rely on to filter for interviews. From a talent perspective, Workera provides a unique opportunity to break into the field irrespective of where (and if) they had college education.
The exciting bull (unicorn) case for the startup is: Workera can become to Data/AI what CFA is to investment management and GRE/GMAT/SAT is to university admissions. And Workera’s assessment model scales, has a large growing market and has zero marginal cost.
Becoming a gold standard in assessment requires credibility from employers. CFA after all has been around for more than 70 years. And here, Workera has an immense unfair advantage - it is founded by AI superstars Andrew Ng (Founder of Coursera, Deeplearning.ai) and Kian Katanforoosh. It is estimated that more than 2mn learners have enrolled in the past decade to Andrew Ng’s AI course on Coursera. This is a big headstart for the company and is not replicable. Currently it runs several data-AI courses at different difficulty levels via Deeplearning.ai.
Andrew and Kian have basically covered the full ‘skilling journey’ for Data/AI between Workera and Deeplearning.ai
The go-to-market model is brilliant. The assessment will be free (and this is viable due to zero marginal cost) and Workera can in the future monetize job placements (paid by the employers). This is a very high margin opportunity and allows Workera to invest in research and development to make the assessment better and better.
The company has a running clock on its website and so far, across test takers more than 1.8mn skills have been tested in aggregate. This seems to be growing quite fast.
Workera will likely have three very good monetization models:
Companies pay Workera to access talent who have high skill scores
Companies pay Workera to provide testing platform for job applicants in Data/AI roles
Companies use Workera to benchmark its talent with other companies and identify upskilling opportunities for its opportunities
Techcrunch reports that Workera has signed up around 30 corporate customers like Siemens and is gaining traction in the enterprise channel.
The free assessment for individuals also helps in creating a large database of test takers against which benchmark numbers will be even more impactful.
I imagine that in the future, an enterprise account of Workera will have an employer’s in-house talent sign up to assess and learn AI skills with leaderboards and gamification/rewards to motivate them.
This may require some mindset shift but likely to happen as companies figure out this new field. Workera’s founders promote this mindset called ‘AI+X’ where they encourage subject matter experts to add AI in their toolkit to gain an edge. As they argue:
“AI+X individuals can hit the ground running, enabling companies to tackle new business opportunities more rapidly. Training a biotech engineer in AI could take months, but training an AI practitioner to understand biotechnologies could take years before meaningful output. A subject matter expert who comes with, or can develop, AI skills is a much better investment.
As an AI+X professional, you are uniquely qualified to build application-specific models even if you aren’t an AI expert. You understand the historical and technical rationale for various decisions, but also will be mindful of the limitations of machine learning. AI experts will help you make those models available to a wider audience.
In medicine, if you were to build a model to classify whether a patient needs a type of high-risk cancer surgery, the model might have an accuracy of 95%. As a data scientist, you can’t tell whether that number is good or bad. But if you are also someone who understands oncology you would understand the implications of a 5% false positive/negative result for patients, as well as the problems faced by a hospital or doctor who orders unnecessary operations. You would know which metrics and outcomes are acceptable and where the model must be further optimized. This nuance is a critical part of building and deploying successful AI models and an instinct that can only be tuned with domain expertise.”
Workera once established as the gold standard for assessment, in the future can also build the ‘talent black book’ for Data/AI - where people can showcase not only their scores but also their projects and can connect with each other. Collaboration opportunities for talent in this growing field will be beneficial.
To summarize: there are going to be millions of new jobs created in Data-AI roles and a lot more people need to be skilled/upskilled in this field. There is a need for a gold standard in assessment of Data-AI skills. Workera has a strong head start to be that gold standard with its product and go-to-market approach and with the unfair advantage of having world renowned experts with lots of industry credibility as founders. Workera’s model can scale and has strong monetization opportunities. Workera is an exciting High Potential Startup to watch out for.