You Don’t Need a PHD to Be a Data Scientist

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In this special guest feature, Head of Data Science Operations, Amy Sharif, at Decision Intelligence company Peak, offers her thoughts on how hiring a diverse group of teammates is critical, especially in 2022, when collaboration, creativity and business smarts are all essential components of a quality data scientist. Amy has led digital transformation projects for global retail brands including Nike, Footasylum, and Superdry, building AI-powered solutions that drive commercial outcomes. Passionate about making STEM careers more accessible, particularly to women, Amy co-founded Peak’s Data Science Graduate Scheme which this year welcomed its first cohort of graduates. She has also spearheaded a mentoring programme, which saw her team of 60 data scientists provide one-to-one coaching to MSc students at North West University.

The demand for data scientists is growing fast, and recruiters are struggling to find qualified candidates to fill open roles. Which begs the question, what makes a qualified candidate?  

Women across the globe make up 55% of university graduates yet account for only a third of STEM degrees. Despite a surge in the data science market, women hold just 18% of industry jobs in the United States as well. Meanwhile, only 7% of the people who earn STEM degrees are Black, and just 12% are Hispanic. 

With these inequities at play, the current shortage of candidates isn’t a simple issue of supply and demand. In my experience, there’s no lack of people in the job market with valuable data science skills. Rather, it’s that companies are used to hiring from a very limited academic pipeline. 

It’s in everyone’s best interests to widen the hiring pool and diversify candidates. It’s not only essential to ensure that machine learning (ML) and Artificial Intelligence (AI) models being created are fair and representative, but also that minorities are not excluded from an increasingly pivotal industry; the US Bureau of Labor Statistics predicts that jobs requiring data science skills will rise by 28% through 2026.

Expanding Hiring Criteria to Meet the Moment

I believe recruiting is no longer just about degrees. Rather, it’s about businesses needing to evaluate employees based on skills and the value they can add – beyond their technical knowledge. Hiring a diverse group of teammates is critical, especially in 2022, when collaboration, creativity and commercial understanding are all skills that are as important to data scientists as coding.

In businesses today, there’s growing demand for AI that surpasses the theoretical — AI that can be applied in a commercial setting, and drive business success. For this reason, it’s imperative businesses improve access for candidates outside the STEM higher-education pipeline. There’s increasing demand for data scientists with commercial know-how or folks who are great at collaborating across an organization to implement AI in different verticals. 

There’s no question having a knowledge of certain programming languages is the base level for every data science role. However, the narrower the requirements, the narrower and more monolithic the candidate pool. In my experience, broadcasting the many shapes and sizes of a modern data scientist will make the field more accessible to a diverse range of candidates. 

Investing in a Diverse Pipeline 

Companies should also broaden the requirements for entry-level programs beyond STEM degrees. As commercial investment in AI increases and the field of data science matures, there will be an increased focus on end-to-outcome, i.e., building models that address a certain need. Data science teams will need to shift from a bottom up approach and instead prioritize getting an application live and generating value as quickly as possible, then focus on iterating and improving it. 

Given the broad array of skills that make a great data scientist, hiring from adjacent fields, like psychology, can open the door to really strong candidates. Expanding the focus beyond technical skills in the recruitment process will help employers find talent that can respond to the increasingly commercial skills needed in these roles. 

Shifting Your Strategy to Stay Resilient and Relevant 

Building these pathways for diverse talent is an essential next step as companies set diversity goals for their workforce. Widening the hiring pool only allows for new, more eclectic visions that ensures all data science models being created are fair and representative of the population as a whole. With different backgrounds and opinions, diversified candidates also bring different experiences and knowledge that will help drive innovation 

Companies who adapt their talent strategy and adjust to the changing workforce are the ones that will maintain a competitive edge. 

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