1.0
Data hiring
Hiring a Data Scientist for a Pharma or Biotech Team
A data scientist in pharma has to do more than build a good model. They work with data that came from experiments, patients or production lines, and they have to explain the result to scientists and, at times, to regulators. If you work with data science recruiters, the first job is to say which kind of data scientist you need, because in life sciences one title stands for several quite different jobs.
1.1
The market
What the labor data says, and what it does not
This guide sets out those roles, what the labor data shows and why the hire is harder to get right than the job title suggests.
Government data covers data scientists across all industries, not pharma alone, so use it as a measure of competition rather than a pharma benchmark.
- Growth: the US Bureau of Labor Statistics (BLS) projects employment of data scientists to grow 35% from 2025 to 2035, much faster than the average for all occupations, with about 24,800 openings a year. Many of those openings come from people leaving the occupation, not only from new jobs.
- Pay: the median annual wage was $120,230 in May 2025. The middle half earned between $85,660 and $158,880, and the top 10% earned more than $199,130.
- Where pay varies: BLS state medians differ widely, from under $70,000 in some states to $163,350 in Washington. A pharma hub such as Massachusetts is well above the national figure.
We could not find a published pay series for data scientists in pharma alone. Treat the BLS range as a starting point and check it against offers in your own region and function.
Sources: US Bureau of Labor Statistics, Occupational Outlook Handbook: data scientists (last modified August 27, 2026); BLS Occupational Employment and Wage Statistics, data scientists (May 2025).
1.2
The roles
Three jobs behind one title
Pharma teams use "data scientist" for work that has little in common. Name the job in your brief. The first two rows rest on published sources. The third is our description of research roles.
| Role | The work | What sets it apart |
|---|---|---|
| Clinical data scientist | Reviews and analyzes data from clinical trials, looks for patterns across sites and patients, and supports risk-based study decisions | Knows trial design, clinical data standards and the rules for trial data |
| Statistical or clinical programmer | Builds the standardized datasets and analyses used in regulatory submissions | Works to FDA-required data standards; mistakes cost review time |
| Research or translational data scientist | Analyzes lab, omics or other research data to help scientists decide what to test next | Needs enough biology to question the data; often works in Python or R |
Sources and notes: for clinical data science, SCDM, "Clinical Data Science"; for the programming row, FDA, Study Data for Submission to CDER and CBER.
1.3
Clinical data science
What it is and why the label matters
The Society for Clinical Data Management (SCDM) treats clinical data science as an evolution of clinical data management, and says it should not be confused with data science in general. In SCDM's words, clinical data management focuses on data flow and data integrity, while clinical data science adds the dimensions of data risk, meaning and value, so the data is credible and reliable.
SCDM's 2020 reflection paper sets the two side by side. The table summarizes a few of its contrasts.
| Clinical data management | Clinical data science |
|---|---|
| Achieves data integrity | Achieves data quality |
| Quality controls | Quality by design |
| Standard processes across studies | Risk-based processes tailored for each study |
| Low volume of data and sources | High volume of data and sources |
| Traditional programming (SQL, SAS) | Machine learning tools (Python, R) |
SCDM went further in 2026. It announced a Certified Clinical Data Scientist (CCDS) credential for experienced professionals whose work reaches across clinical research, analytics and technology. A certificate is not proof that someone can do your job, but the label tells you the field now treats the role as distinct from data management.
For you, this means the same words on a CV can mean different things. A "data scientist" from a software company and a clinical data scientist from a CRO may share Python and share little else.
Sources: SCDM, Clinical Data Science; SCDM reflection paper, CDM role evolution, part 3 (2020); SCDM, "Introducing CCDS" (September 2026).
1.4
The standards
Why clinical programming candidates must know CDISC
For one of the three roles, the standards are not optional. FDA requires study data in certain submissions to follow the standards listed in its Data Standards Catalog. The supported formats include CDISC's Study Data Tabulation Model (SDTM) for clinical data, the Analysis Data Model (ADaM) for analysis datasets and Define-XML for the metadata. FDA may refuse to file a New Drug Application or Biologics License Application whose study data do not conform.
The requirement attaches to the study start date. FDA states it applies to studies that started after December 17, 2016 for NDAs, BLAs and ANDAs, and after December 17, 2017 for commercial INDs. Check the current catalog for specific versions.
That is why a general-purpose data scientist is often the wrong hire for a submission-data role, however strong the CV. Experience with these standards comes from having built them for real studies.
Sources: FDA, Study Data for Submission to CDER and CBER; FDA, Study Data Standards Resources (page dated February 2025).
1.5
The difficulty
Why a pharma data scientist is hard to hire well
Pharma data science fails in ways a CV rarely shows. A technically strong candidate who does not understand how trial, assay or manufacturing data is produced can build clean-looking analysis on data that cannot support it. SCDM's papers describe the shift from managing data to judging its risk, meaning and value, and call for deep knowledge of data context, quality and source. The standards add another layer for submission work, and a model that supports a regulatory decision brings its own expectations: FDA's January 2025 draft guidance on AI to support regulatory decision-making asks sponsors to define the "context of use," the specific question the model is there to answer. FDA's page labels it a draft. Our guide to hiring a machine learning engineer covers that side of the work and explains how ML engineers differ from data scientists.
So the hire has to combine statistics, programming, domain knowledge and the habit of explaining results to scientists who will challenge them. Few candidates have all four, and the market data above shows how many other industries want the same people. Telling a candidate who truly has them from one who only lists them is the screening work a specialist recruiter does before you meet anyone.
Sources: SCDM reflection paper, 2019 (published March 2022 on the SCDM site); FDA, draft guidance on AI to support regulatory decision-making for drugs and biological products (January 2025).
1.6
Hire or grow
Hire a data scientist or retrain a data manager?
SCDM's papers say that clinical data management leaders should help staff move from clinical data manager to clinical data scientist, so retraining is a real route. It works when a company has a senior person to mentor and a project that can wait. It struggles when the gap is statistical or machine learning depth that nobody on the team can teach. If you decide to hire, specialist data scientists are a small pool in demand across every industry. That is where a recruiter earns a fee, and our guide to recruitment agency fees explains how those fees work.
1.7
Geeqers
How Geeqers handles a data science search
Geeqers is a US recruiting firm for pharma, biotech and medical-device companies, and AI and data roles are one of the areas on our employer page. This is how a search works with us, as we describe it on that page:
- We screen every candidate ourselves. We search the market and speak to each person before you hear their name.
- A shortlist within 7 business days of a complete brief. For a rare profile the list may hold two people instead of five, and we tell you that on day zero.
- No fee until your hire starts work, and nothing if nobody starts. The fee is agreed with you in writing before we begin.
- A 90-day replacement. If your hire resigns, or you let them go for performance reasons, within 90 calendar days of their start date, we run the search again at no extra fee. The conditions are in the agreement you sign first.
- Confidential searches on request, useful when a new data hire signals a program you have not announced.
If you have a role to fill, send us a hiring brief. Tell us whether the person will work on trial data, research data or submission datasets, and we will take it from there.
1.8
FAQ
Frequently asked questions
What does a data scientist do at a pharma company?
They analyze data from trials, research or manufacturing to help people decide what to do next. The work differs a lot by function, so define whether the role is clinical, research or submission-focused before you write the job description.
What is clinical data science?
SCDM describes it as an evolution of clinical data management. It adds analysis of risk, meaning and value in clinical trial data to the traditional focus on data flow and integrity. It is narrower than data science in general.
Do pharma data scientists need to know CDISC?
Only for roles that build or check submission datasets. FDA requires study data in certain NDA, BLA and ANDA submissions to follow standards such as SDTM and ADaM. A research data scientist may never touch them.
How is a data scientist different from a machine learning engineer?
Titles overlap, so describe the work instead of the label. Broadly, a data scientist leans toward analysis and modeling, and a machine learning engineer toward building and running models in production. We cover the second in our machine learning engineer guide.
How much does a data scientist cost to hire?
BLS puts the national median wage at $120,230 as of May 2025, with the middle half between $85,660 and $158,880 across all industries. We found no pharma-only series, so check local offers. A recruiter's fee is separate; with Geeqers it is agreed in writing before we start and is due only after your hire starts work.
How do data science recruiters work in pharma and biotech?
A specialist data science recruitment agency or data science staffing firm searches the market, screens candidates and presents a shortlist. Fee models differ between firms, and we explain the common ones in our guide to recruitment agency fees. With Geeqers the fee is agreed in writing before we start and is due only after your hire starts work.
Can a data science search be kept confidential?
Yes. Geeqers runs confidential searches on request, so your current team and competitors do not need to know you are hiring.
Image: "Woman working on computer at laboratory" by Mikhail Nilov via Pexels, used under the Pexels licence. Figures and guidance details were checked against the sources linked above in October 2026.
