As we advance towards a data-driven future, diversity and inclusion in AI is no longer a nice-to-have. We have reached the point in time where humanity and technology co-exist and our lives get more intertwined with data in one way or another.

The role of the data scientist is increasingly a necessity across all sectors. Many young professionals are jumping on the trending demand in employment and quickly seeking out the “top ten must-have skills in AI” or courses promising that graduates will “become a data scientist in six months.”

Data scientists alone are not enough for future of AI

While we welcome the greater interest and awareness in data, the deluge of those at the end of the straight, singular path of becoming a data scientist doesn’t bode well for the future of AI. The data science lifecycle requires many more skills and roles beyond machine learning (ML) engineers. Becoming a data scientist isn’t necessarily the only or best way to be part of this future data-driven AI journey. I can certainly attest to that.

I have spent over a decade in technology, moving from science-based health projects to pure technology across three countries, without being prescriptive about the kind of skills needed for a career in data. My real passion was and remains mathematical storytelling. I studied Mathematics, mechanical engineering and studio art. I enjoyed ballet dancing and did a stint of modelling as well as served as an advisor in ML for a non-profit organisation while holding down a full-time job in technology. I now swim and paint outside of my role at GitLab, where I work on bridging the gap between data science and DevOps, to make machine predictions work for customers.

By combining my analytical inclination, my passion for math and solving problems as well as my creative and artistic pursuits, I have been able to find myself working on game-changing innovation like building robotic arms for smart prosthetics, delving into the world of insights for large technology companies and now deciphering the next stages of machine learning deployment in software development.

Non-technical skills should complement robotic skills

The future of AI will require not just Python coding, data and engineering skills but more importantly, there will be a need to emphasise judgment, decision-making and people skills.

In our day-to-day lives as data professionals, we have to be active listeners to understand the needs of customers, their problems and their desires. Only with that understanding can we creatively craft the analytics solution to solve the need and articulate how the solution fits in the holistic journey of the customers.

While there is no denying that enhancing our technical skills is paramount, I believe that skills such as critical thinking, communication and decision-making are equally important.

Employers often have a checklist of the robotic skills when hiring a data scientist. However, to build a good AI model, one not only needs a mathematician, but also poets, storytellers, linguistic specialists, among others.

Instead of viewing analytics and soft skills as two distinct skill sets, they should be considered as part of the same genre of human problem-solving skills. How we use tools is the craft but how we apply these tools to creatively solve a human problem is an art. Analytics is therefore a subset of soft skills and vice versa.

In order to achieve the true potential of an AI-driven world, there needs to be a broader and deeper understanding of the role of data science by organisations in order to correct its misrepresentation as a professional field for young professionals. Coding languages and data principles are just one part of the puzzle but how technology will develop in the future requires a breakdown of the obstacles, barriers and biases in the IT workforce at the enterprise level.

Through AI, we now have the capability to realise the true potential of technology in helping humans make better decisions and solve problems faster. We can achieve this effectively, collectively when AI is more inclusive, where passion and individuality is embraced and creatively used in unified machine prediction and storytelling.


About Mon Ray:
Monmayuri Ray (Mon), Solutions Architect at GitLab is focused on making tech work for customers and solving problems at the intersection of AI, machine learning and DevOps. She has worked as a data scientist for Microsoft, eBay, Quantium and Deloitte. Mon’s passion is mathematical storytelling, and she studied an unusual combination of applied maths, engineering and studio art in the U.S. and Hong Kong, followed by projects which combined her analytical and creative talents such as building robotic arms for smart prosthetics. Mon is passionate about diversity and its critical role in shaping the future of data.