Job brief
We are seeking a rigorous Biostatistician to join our clinical development team and play a pivotal role in the success of our upcoming Phase II and III drug trials. You will be responsible for defining statistical analysis plans, validating data integrity, and contributing to the evidence packages that support new drug applications. If you are passionate about medical innovation and thrive on transforming complex datasets into clear, regulatory-ready insights, we want to hear from you. Join us to influence the future of patient care through disciplined statistical science.
Key highlights
- Develop and finalize comprehensive Statistical Analysis Plans (SAPs) for Phase I-IV clinical trials in alignment with study objectives.
- Perform complex statistical programming in SAS or R to generate high-quality TLFs for clinical study reports and integrated summaries.
- Validate clinical trial databases, ensuring all data outputs are compliant with CDISC SDTM and ADaM data standards for regulatory submissions.
- Calculate power and sample size requirements to ensure clinical studies are sufficiently robust to meet primary and secondary endpoints.
What is a Biostatistician?
A Biostatistician is a highly specialized quantitative scientist who designs clinical studies and analyzes complex biological and medical data. By utilizing sophisticated statistical methodologies, a Biostatistician ensures that findings from clinical trials are robust, reproducible, and compliant with global health authority standards. Their technical expertise bridges the gap between raw experimental data and actionable insights that dictate the success or failure of new pharmaceutical therapies.
What does a Biostatistician do?
A Biostatistician spends their time designing study protocols, calculating necessary sample sizes, and selecting appropriate statistical models to analyze clinical trial endpoints. They use programming languages such as SAS or R to clean datasets, generate CDISC-compliant tables, listings, and figures (TLFs), and perform rigorous hypothesis testing. Frequent collaboration with clinical research physicians, regulatory affairs specialists, and data management teams is essential to ensure that clinical study reports meet the stringent evidentiary requirements for FDA or EMA drug submissions.
Key responsibilities
- Develop and finalize comprehensive Statistical Analysis Plans (SAPs) for Phase I-IV clinical trials in alignment with study objectives.
- Perform complex statistical programming in SAS or R to generate high-quality TLFs for clinical study reports and integrated summaries.
- Calculate power and sample size requirements to ensure clinical studies are sufficiently robust to meet primary and secondary endpoints.
- Validate clinical trial databases, ensuring all data outputs are compliant with CDISC SDTM and ADaM data standards for regulatory submissions.
- Collaborate with clinical trial managers and medical monitors to interpret statistical results and draft clear, accurate clinical study reports.
- Provide expert statistical oversight for Data Monitoring Committees (DMC) to ensure patient safety and data integrity throughout study execution.
- Prepare response packages for regulatory queries from the FDA, EMA, or PMDA regarding clinical methodology and efficacy findings.
- Implement Bayesian or frequentist statistical techniques to optimize trial design and minimize bias in multi-center international study environments.
Requirements and skills
- Master’s degree or PhD in Biostatistics, Statistics, or a closely related quantitative field with a focus on clinical research.
- 3+ years of professional experience as a Biostatistician within the pharmaceutical or biotechnology industry.
- Advanced proficiency in SAS programming, including Base SAS, SAS/STAT, and SAS/GRAPH, alongside experience with R or Python for statistical modeling.
- Deep working knowledge of CDISC standards (SDTM and ADaM) and their implementation in regulatory submissions.
- Proven understanding of ICH-GCP guidelines and regulatory requirements for drug approval pathways across multiple global regions.
- Demonstrated ability to communicate complex statistical methodologies to non-statistical stakeholders, such as clinical investigators and project managers.
- Experience using clinical trial management systems (CTMS) or electronic data capture (EDC) systems like Medidata Rave or Inform.
- Strong analytical problem-solving skills with an ability to manage multiple high-priority clinical projects under strict timelines.
FAQs
What does a Biostatistician do in clinical trials?
A Biostatistician is the architect of the data analysis strategy in clinical research. They design the trial protocols to ensure statistically sound results, perform the data analysis using specialized software like SAS, and interpret the findings to determine whether a drug is safe and effective for patient use. Their work is the mathematical foundation upon which regulatory bodies approve life-saving medications.
What skills are required to become a Biostatistician?
Essential skills include a deep mastery of statistical theory, such as survival analysis, longitudinal data analysis, and hypothesis testing. Candidates must also possess high-level programming proficiency in SAS or R, a strong grasp of CDISC data standards, and an understanding of FDA and ICH-GCP regulatory guidelines. Strong technical writing skills are also necessary for drafting the formal clinical study reports submitted to health authorities.
Who does a Biostatistician collaborate with on a daily basis?
A Biostatistician works in a highly interdisciplinary environment, collaborating closely with clinical research associates, data managers, medical writers, and clinical leads. They also interact with regulatory affairs professionals to ensure submissions meet international standards and occasionally present their findings to external Data Monitoring Committees or government regulatory reviewers.
Why is the role of a Biostatistician critical in the pharmaceutical industry?
The Biostatistician is critical because they provide the objective, mathematical evidence required to prove that a new drug provides a clinical benefit without undue risk. Without their analysis, pharmaceutical companies would be unable to secure regulatory approval, and patients would not gain access to new treatments. Their rigor in handling and interpreting data ultimately dictates the speed and safety with which medical innovations reach the public.