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.
- 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.
- 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.
- 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.
- 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.
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.
