By Jennifer Maffia, Owner of Advanced Recruiting Partners 

Over the years, I’ve watched the definition of a “great candidate” change considerably across life sciences. 

Data science is one of the clearest examples. 

When I talk with hiring managers today, they aren’t simply looking for someone who knows Python, R, SQL, or the latest machine learning tools. Those skills absolutely matter, but they’re only part of the picture. 

What companies really need are people who can take all of that technical knowledge and apply it to the science, the business, and the very human questions behind the data. 

That combination is becoming incredibly valuable in biopharma. 

The Most Valuable Candidates Can Operate in Two Worlds 

Some of the most interesting candidates I see today sit at the intersection of science and technology. 

On one side, you have scientists who have expanded their computational capabilities. On the other, you have highly skilled data professionals who have developed an understanding of biology, medicine, or drug development. 

That intersection is becoming incredibly valuable. 

A candidate can be exceptional with Python, R, SQL, machine learning, or cloud-based technologies. But in a life sciences environment, technical ability alone doesn’t necessarily mean someone can understand the significance of the data they are analyzing. 

The reverse is also true. Deep scientific knowledge is valuable, but companies increasingly need professionals who can work with complex datasets and modern computational tools. 

The strongest candidates can connect the two. 

Technical Skills Are Only Part of the Equation 

There are certainly technical capabilities showing up repeatedly in data science searches. Python, R, SQL, data infrastructure, cloud technologies, artificial intelligence, and machine learning all matter. 

But the exact combination depends on the problem a company is trying to solve. 

A data scientist supporting genomics may need a very different background from someone working with clinical trial data, biomarkers, real-world evidence, or commercial analytics. 

That’s why I encourage hiring managers to resist treating “data scientist” as one standardized profile. 

Before building the job description, ask: What do we actually need this person to accomplish? 

That answer should drive the requirements. 

Don’t Underestimate the Ability to Translate 

One skill that doesn’t always receive enough attention in these searches is communication. 

Data scientists rarely work only with other data scientists. They may need to collaborate with biologists, clinicians, clinical operations teams, regulatory professionals, executives, and other stakeholders who don’t share the same technical vocabulary. 

Being able to produce a sophisticated analysis is one thing. Being able to explain what it means, why it matters, and what should happen next is another. 

When I’m evaluating talent, that ability to translate complexity stands out. 

Two candidates may have comparable technical backgrounds, but the person who can make their expertise useful and understandable across an organization can bring tremendous value. 

Be Careful Not to Hire for a Unicorn 

As demand increases, there is a temptation to put everything imaginable into the job description: advanced degrees, extensive industry experience, deep scientific expertise, multiple programming languages, AI and machine learning capabilities, specific platforms, and years of experience in exactly the right therapeutic area. 

Eventually, the candidate you’re describing may barely exist. 

Instead, separate what someone must know on day one from what a talented person can learn. 

There will absolutely be positions where highly specialized expertise is nonnegotiable. But there will also be situations where adjacent experience, intellectual curiosity, and a demonstrated ability to learn are more valuable than checking every box. 

The goal shouldn’t be finding the person with the longest list of technical skills. It should be finding the person whose skills align with the problem you’re trying to solve. 

What This Means for Candidates 

For data scientists interested in biopharma, my advice is similar: don’t simply tell employers which technologies you know. Show them what you’ve done with them. 

What problem were you solving? What did you discover? How did your analysis influence a scientific, clinical, or business decision? 

If you’re transitioning into life sciences from another industry, demonstrate that you’re learning the science and understanding the environment in which your skills will be applied. 

And perhaps most importantly, practice explaining your work to people who aren’t data scientists. 

That may ultimately distinguish you more than adding another tool to your résumé. 

Biopharma Doesn’t Just Need More Data Scientists 

The industry’s growing demand for data expertise is real, but solving it isn’t simply a numbers game. 

Biopharma needs professionals who can connect data, science, and people. 

For hiring managers, that means becoming more precise about the problems they need talent to solve. For candidates, it means demonstrating not only technical capability but scientific curiosity, communication skills, and measurable impact. 

The future of biopharma may be increasingly data-driven, but turning all that data into meaningful progress will still depend on finding the right people to make sense of it. 

About Jennifer Maffia 
With over 20 years of experience in clinical staffing, Jennifer Maffia connects pharmaceutical, biotech, and life sciences companies with top-tier clinical talent. She is known for building lasting client relationships, supporting tenured recruiters, and driving impactful hiring strategies. Through industry partnerships and active board involvement, Jennifer remains committed to advancing the life sciences field and improving patient outcomes.