Biostatistician

Biostatisticians are the people who decide whether a clinical trial result is real. They design the study to have enough statistical power to detect a meaningful treatment effect, write the analysis plan that specifies exactly how the data will be analyzed before anyone sees the results, run the pre-specified analyses, and report them honestly — including when they show the drug doesn't work.

In pharmaceutical drug development, a biostatistician is assigned to a clinical development program early — often at the Phase I stage — and stays with it through regulatory submission. They're involved in endpoint selection (what measurement will serve as the primary evidence of efficacy?), randomization design (how do you balance known confounders across treatment groups?), interim analysis planning (when and how do you look at accumulating data without inflating false-positive error rates?), and statistical analysis plan authorship.

The SAP is the document that matters most. It must be complete enough that an independent statistician could reproduce the analysis from the data alone, locked before unblinding, and specific enough that it prevents cherry-picking favorable analyses after seeing results. Writing a good SAP requires anticipating analysis challenges before the data exist — missing data patterns, protocol deviations, unexpected distributions — and pre-specifying how they'll be handled.

FDA interaction is part of the senior biostatistician's role in a way it isn't for many scientific disciplines. Type B meetings before NDA submission include formal statistical discussions of analysis methodology. FDA statistical reviewers write independent analyses of submitted data and challenge analysis choices they find unconvincing. Understanding how FDA statistical reviewers think, what they're likely to question, and how to structure analyses to withstand that scrutiny is a career-long learning process.

Education:

• M.S. in biostatistics or statistics (entry level to mid-level positions in industry)
• Ph.D. in biostatistics or statistics (expected for senior and principal statistician roles at pharmaceutical companies)
• Strong mathematical foundation: probability theory, linear algebra, real analysis

Statistical methods knowledge:

• Clinical trial methodology: parallel group, crossover, and adaptive designs; randomization and stratification
• Hypothesis testing framework: Type I/II error control, multiplicity adjustment (Bonferroni, Holm, Hochberg, hierarchical testing)
• Survival analysis: Kaplan-Meier estimation, log-rank test, Cox proportional hazards model
• Mixed models for repeated measures (MMRM) — the standard method for continuous endpoints in most Phase II/III trials
• Missing data methods: multiple imputation, tipping point analyses, reference-based imputation
• Bayesian methods for adaptive designs, sample size re-estimation, hierarchical models

Programming:

• SAS (PROC MIXED, PROC LIFETEST, PROC PHREG, macros) — industry standard for regulated clinical trial analysis
• R (clinical trials packages: survival, nlme, lme4, ggplot2, rtables) — increasingly accepted
• CDISC standards: SDTM domain structure, ADaM ADSL/ADAE/ADTTE/ADLB dataset specification

Regulatory knowledge:

• ICH E9(R1) estimands framework (requires understanding the estimand, estimation strategy, and sensitivity analysis)
• ICH E8(R1) general considerations for clinical studies
• FDA statistical review guidance documents for specific disease areas

Biostatistics is one of the most stable and well-compensated quantitative science careers, and the pharmaceutical and biotech industry's persistent demand for qualified biostatisticians is unlikely to change. Every clinical trial requires statistical design and analysis, and FDA will not accept NDA or BLA submissions without complete statistical documentation. That regulatory mandate is the floor of demand.

The supply of trained biostatisticians has historically lagged behind demand. Graduate biostatistics programs are relatively small, and the academic pipeline cannot expand quickly enough to meet pharmaceutical industry needs. This persistent supply constraint is a primary reason that mid-career biostatisticians with NDA experience can command salaries well above $150K at major pharmaceutical companies.

Clinical stage biotechs — particularly at the Phase III stage where a pivotal trial is running — have intense statistical needs and typically solve them by hiring CRO statisticians, hiring contractors, or paying for experienced statisticians from larger pharma. Startups with recently-funded Phase III programs often discover that they've underestimated how much statistical support they need when FDA review approaches.

Real-world evidence (RWE) has grown into a substantial practice area within pharmaceutical statistics. Analyzing administrative claims, electronic health records, and disease registries to generate evidence for drug labels, post-marketing commitments, and health technology assessments requires different statistical skills than classical randomized trial analysis — propensity scoring, instrumental variables, target trial emulation — and biostatisticians who work in RWE alongside traditional trial statisticians are in demand.

For biostatisticians who want to work closer to the science and less on production analysis, early phase and exploratory statistical work — dose-finding models, Bayesian Phase I/II designs, biomarker analysis for patient selection — offers more methodological novelty and direct drug development impact. The career path from this work leads to statistical science leadership rather than standard statistical operations.

Dear Hiring Manager,

I'm applying for the Biostatistician position at [Company]. I completed my Ph.D. in biostatistics at [University] with a dissertation on multiple imputation methods for non-ignorably missing data in clinical trials, and I have four years of industry experience at [Company], where I've supported two Phase III oncology programs through study completion and NDA submission.

My core responsibilities have been SAP authorship, TLF production in SAS, and CSR statistical section writing. On the more recent program, I led the SAP for a Phase III study with a co-primary endpoint structure that required hierarchical testing to control family-wise error. The FDA statistical reviewer questioned our handling of the time-to-event secondary endpoint in the submitted CSR — I prepared the technical response to the IR, which cleared on the first submission. That interaction reinforced for me how important it is to pre-specify sensitivity analyses that directly address the objections a regulatory statistician is likely to raise.

I've also been the go-to person on our team for missing data methodology. I ran a simulation study comparing reference-based imputation approaches for our primary endpoint that we shared in an internal methods paper and that informed how we specified the main estimand analysis and tipping-point sensitivity.

The [Phase II/III program focus area] work at [Company] is the reason I'm specifically interested in this position. The estimand challenges in [specific disease area — e.g., oncology with treatment switching, CNS with informative dropout] are exactly the kind of statistical problems I want to be working on.

Thank you for your time.

[Your Name]