The Power of Data Science Hybrids

By Andrea Yip on October 29, 2020


After speaking with many data scientists and employers alike, I discovered the concept of “hybrid” data scientists. If you are starting your career as a data scientist, framing yourself in this manner can help you get your foot in the door. It’s a powerful way to distinguish yourself from other applicants.

“Hybrid” data scientists are individuals who have made a pivot into data science (as scientists, analysts, engineers, etc.) from a non-data profession. Today it’s common to see hybrids who have entered data roles after studying and building careers in non-data professions like nursing, academia, sales, software engineering, or marketing. With the burgeoning demand and growth of data science as a field, employers are seeking to build out their data capabilities, while professionals are beginning to pivot into new career opportunities. At the same time, a growing ecosystem of data-focused courses, bootcamps, degrees and certificate programs are enabling these professionals to rapidly up-skill and re-skill.

This constellation of trends has produced a wave of hybrid data talent. The value of these individuals is in their former experiences and subject matter expertise – and it’s truly a strength to leverage when competing for jobs. Today, the nature of data science work is diversifying. As data capabilities grow across companies, there is greater demand for these teams to translate numbers and models into business results and apply them to the cross-sector functions and needs of a company. Consequently, data teams require a mix of highly technical expertise alongside those specialize in socializing and translating results into real world contexts.


Examples of Hybrid Data Scientists

Take for instance three hybrid individuals who pivoted into data driven roles:

  • Hamza: A customer sales representative working in retail took online courses and convinced his company to let him take on smaller data analytics projects in the sales department. Eventually, he was promoted to a data scientist because not only could he understand the data, he was comfortable in customer-facing roles and knew how to effectively communicate what his team’s results would mean for sales executives across the organization.
  • Allison: A young professional who studied and worked in supply chain management pivoted to become an analyst and was specifically recruited to work on a data science team that was focused on supply chain optimization at a large CPG company.
  • Mina: An academic who moved into the tech industry by leveraging the technical research experience she got through her PhD in neuroscience, completing a 3-month online data bootcamp, and working as an intern at a large tech company. Today she helps lead digital transformation at a healthcare company that makes medical devices for patients facing mental health challenges.


How to stand out in job applications

It’s the job of the hybrid data scientist to describe how their past experiences are relevant and add business value to employers. An employer will not connect the dots themselves. It’s critical for the candidate to tell their own story and justify how their unique and possibly unconventional background serves as an asset to the business. As employers are increasingly faced with hundreds if not thousands of candidates vying for data science and AI jobs, with the right story, hybrids stand out of the crowd.


Advice to hybrid job seekers

If you are pivoting your career from another industry or area of expertise, frame your past experiences as a strength in your data science job search. In fact, many employers would prefer some of these more complex experiences over pure technical expertise – it shows you can empathize with the data and problems being solved at a company.

If you really want to stand out, embrace your past experiences by emphasizing them in your data science portfolio. For instance, show end-to-end projects that demonstrate how you can apply your practical industry know-how in a data science context.

To hear from a hybrid data scientist, check out Jorge Escobedo’s fireside chat on how he transitioned from a PhD in string theory to applied ML. Today Jorge serves as the VP of Engineering for Drop Technologies. Check out my talk on hybrid data scientists and storytelling for more of my thoughts on this topic.