The Key to Hiring Data Scientists for Pharma

Data analytics is one of the hottest fields out there right now. Businesses across all industries need to take advantage of the tsunami of customer data available to them, and there simply aren't enough qualified, experienced data analysts to fill that need.

For pharma, data means more than just creating warm and fuzzy experiences for consumers. Much more. It's about human lives. Integrating data across all healthcare platforms over a significant period of time — patient records, pharmacy records, clinics, lab results and more — can lead us to a new level of patient health. It enables pharmaceutical managers to develop brand strategies based on a holistic view of the customer (patient, payer, prescriber) experience without having to make leaps of faith across disparate data sources. From a patient outcome perspective, integrated data can improve the efficiency of clinical trials, and pharmacy data can even become a real-time predictor of a patient's health.

The tech industry has been hiring data analysts for a while now, people who have skills in math, statistics, machine learning, coding, domain expertise and software engineering. Of those, the big three are math, software or coding, and domain expertise.

If that person sounds like a unicorn, it's because they are. You find someone who has all of that rolled into one CV, and you've found the Holy Grail. And it's about as likely. Even if you do find them, good luck keeping them. People with experience are holding the golden ticket in the job market.

So, we're looking at needing a unicorn that everyone else needs, too. Not an ideal situation. The solution? Building a team. You don't need someone who is an expert in all of those areas. You need a team of people who are experts in each of the areas.

Here's what each of those experts can bring to your analytics team, and where they might be coming from:
  • Software expertise: You'll find these people in tech companies. They'll bring algorithm design, database creation, analytics pipelines, coding, programming language expertise and other software-related skills to the table.
  • Math or domain data science: They'll typically have worked in large companies in marketing, sales or other areas where customer data is key. Or they may be coming from academia. You may find PhDs in math or statistics. They'll add statistical analysis and knowledge of machine learning to your team.
That applies to all industries. But for pharma, it's a little trickier. The majority of experienced data scientists now come from the technology sector. But the needs of pharma in data analysis are different than the needs of Silicon Valley or even other businesses.

In pharma, data scientists come from behavioral science, economic, market research, even marketing backgrounds. It's not like they haven't done research, but the nature of the research they've done is different. When pharma is hiring data scientists, a tech background isn't enough. What's really important for pharma when hiring data scientists:
  • Knowledge of the industry. They must know the pharma space, the customers and stakeholders.
  • Knowing the nature of our data. Pharma data is unique. Unlike, for example, Facebook data, pharma data is sensitive. It's health information. It takes time for people to understand the nature of the data, its sensitivity and how to use it.
Without those two critical factors, they won't be successful. However, there's room to challenge the belief that our data scientists must be from pharma. Why? The diversity of other industries brings a diversity of thought. It results in:
  • New technology
  • New ways of thinking
  • New creativity
We can train people to understand our industry. And remember, the intense demand for data analytics is new, but the prerequisites for being a data scientist aren't new. Math, computer science, software engineering, coding. Finding people who specialize in one of those things is the key to building a solid analytics team.

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