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Biostatistician
Our client is seeking a Biostatistician to support data quality, analysis, interpretation, and delivery efforts within a large international oncology research network. This individual will collaborate with data managers, researchers, project leadership, and informatics partners to support retrospective clinical research studies utilizing real-world clinical and genomic data.
The ideal candidate is highly organized, analytical, and able to work effectively both independently and within cross-functional teams. This role offers the opportunity to contribute to impactful cancer research initiatives by developing statistical analyses, generating analytical datasets, and producing insights that advance cancer prevention and treatment.
Key Responsibilities
Statistical Analysis & Research Support
- Perform data quality assessments, statistical analyses, and interpretation of clinical and genomic datasets.
- Collaborate with researchers, data managers, project leadership, and external partners on research initiatives.
- Support retrospective clinical research projects utilizing real-world data.
- Develop and execute statistical analysis plans.
- Apply bioinformatics and statistical methodologies to support genomic variant-based clinical studies.
- Conduct time-to-event analyses, regression modeling, and other advanced statistical methods.
- Generate research findings and communicate results to technical and non-technical stakeholders.
- Contribute to novel research analyses focused on oncology and patient outcomes.
- Support study design activities, including cohort definition, endpoint selection, and analytical methodology.
Data Delivery & Statistical Programming
- Develop, validate, and execute complex queries to support sponsored research initiatives, including quality assurance and quality control (QA/QC) activities at the site, cohort, and network-wide levels to ensure delivery of harmonized datasets.
- Collaborate with clinical data management teams to investigate data discrepancies and provide clear documentation of query outputs to support communication with participating research sites.
- Design, develop, and maintain statistical programming code used to generate derived variables and analytical datasets.
- Develop derivations and calculations for:
- Time-to-event analyses and clinical event timelines.
- Oncology outcomes and endpoints, including Overall Survival (OS), Progression-Free Survival (PFS), real-world Response Rate (rwRR), Time to Next Treatment (TTNT), and other study-specific endpoints.
- Classification of disease progression and lesion sites, including differentiation of primary disease, local metastases, and distant metastases.
- Develop and maintain scalable, modular, and reusable code that supports evolving data models, research initiatives, and sponsor requirements.
- Create new derived variables and classification logic as additional cancer types, disease areas, and project-specific requirements are introduced.
- Support expansion into hematologic malignancy datasets through development of specialized derivations and analytical frameworks.
- Ensure all programming deliverables follow established documentation standards, version control practices, and change management procedures to maintain traceability and reproducibility across projects.
- Generate comprehensive analytical data guides for delivered cohorts, including detailed documentation of structured variables, curated variables, derived variables, and derivation methodologies.
- Develop cohort-level release notes documenting updates, enhancements, and data changes between cohort refreshes and subsequent data releases.
- Partner with data management, informatics, and research teams to ensure data assets are analysis-ready and aligned with project objectives.
Required Qualifications
- M.S. or Ph.D. in Biostatistics, Epidemiology, or a related quantitative discipline.
- 3+ years of post-graduate experience applying statistical and analytical methodologies.
- Strong proficiency in R (preferred) or SAS, with demonstrated experience in statistical modeling and statistical programming.
- Proficiency in Python or SQL.
- Experience with biostatistical methods including:
- Survival and time-to-event analyses
- Regression modeling
- Observational data analysis
- Real-world evidence (RWE) methodologies
- Experience contributing to study design, cohort definition, and statistical analysis planning.
- Experience analyzing real-world clinical data.
- Excellent verbal and written communication skills.
- Strong organizational skills and attention to detail.
- Ability to prioritize multiple projects and work effectively in a fast-paced environment.
Preferred Qualifications
- Knowledge of clinical oncology terminology and concepts.
- Experience with clinico-genomic, genomic, or multi-omic data analysis.
- Experience integrating clinical and molecular datasets.
- Experience supporting sponsored research or external data delivery projects.
- Familiarity with cloud-based or distributed data environments such as AWS, BigQuery, or similar platforms.
- Experience developing analytical datasets and derived endpoints in oncology or other therapeutic areas.
Ideal Background
This role is well suited for a biostatistician with experience in oncology, real-world evidence (RWE), clinical research, genomic data analysis, or statistical programming who enjoys working at the intersection of statistics, data science, and translational research. The successful candidate will have a strong balance of statistical expertise, programming skills, data management awareness, and scientific curiosity.