Postdoctoral Research Fellow in Biostatistics and Cancer Genetics
Department of Data Science, Dana-Farber Cancer Institute — Boston, MA
Dana-Farber Cancer Institute (DFCI) is seeking a highly motivated Postdoctoral Research Fellow in Biostatistics to join the Department of Data Science and contribute to collaborative research in cancer genetics and precision oncology.
The primary focus of this position is collaborative and applied biostatistical research. The fellow will work closely with clinical investigators, geneticists, genetic counselors, computational researchers, and biostatisticians on study design, data management and derivation, statistical analysis, interpretation, and scientific communication, with opportunities to contribute to methodological research motivated by the scientific projects.
Approximately 50% of the fellow's effort will be devoted to a newly funded, five-year NIH R01 project focused on the development and validation of prediction models for identifying germline pathogenic variants, using large multi-institutional datasets from Dana-Farber and collaborating cancer centers. The remaining 50% will support collaborative research within the Division of Cancer Genetics and Prevention at DFCI.
How to apply
Applications are accepted on a rolling basis through the DFCI Data Science Postdoctoral Fellows Program. Submit your application through the program's application form and select Hajime Uno as your mentor of interest.
Questions are welcome — write to huno@ds.dfci.harvard.edu.
Responsibilities
NIH-funded R01 project — approximately 50% effort
The fellow will serve as a biostatistician on a multidisciplinary team developing and validating statistical prediction models for germline pathogenic variants:
- Statistical analyses of large clinical, genetic, and genomic datasets.
- Data derivation, cleaning, harmonization, quality control, and integration across institutions and data sources.
- Development and validation of statistical prediction models.
- Evaluation of model discrimination, calibration, clinical utility, and performance across patient subgroups and institutions.
- Simulation studies and computational evaluations.
- Development and evaluation of statistical methods motivated by the project.
- Reproducible statistical programs and analysis workflows.
- Preparation of results, tables, and figures for manuscripts, presentations, and scientific reports.
Collaborative cancer genetics research — approximately 50% effort
The fellow will serve as a collaborative biostatistician for clinical, translational, and observational studies in cancer genetics, hereditary cancer, and cancer prevention:
- Statistical input on study design and research questions; development of statistical analysis plans.
- Data management, data derivation, and statistical analyses.
- Selecting and applying appropriate statistical methods for clinical and biomedical research.
- Interpreting results in collaboration with investigators; preparing tables, figures, and statistical summaries.
- Contributing to manuscripts, abstracts, presentations, and grant applications.
- Participating in multidisciplinary research meetings and communicating statistical concepts clearly to clinical and scientific collaborators.
Qualifications
Required
- PhD or equivalent doctoral degree in Biostatistics at the time of appointment.
- Strong training in applied biostatistical methods, including regression modeling and other methods commonly used in clinical or biomedical research.
- Strong statistical programming skills, preferably in R.
- Experience using Git and GitHub/GitLab for version control and collaborative development of reproducible analysis workflows.
- Experience with data management, data derivation, and statistical analysis of clinical or biomedical datasets.
- Ability to independently conduct analyses from preparation of analysis-ready datasets through interpretation and presentation of results.
- Strong written and verbal communication skills, and an interest in collaborative, multidisciplinary biomedical research.
Preferred
- Experience with prediction modeling, statistical learning, penalized regression, or related methods.
- Experience analyzing large observational, clinical, genetic, genomic, or other complex biomedical datasets.
- Experience working with data from multiple sources or institutions.
- Experience with simulation studies or computational evaluation of statistical methods.
- Experience contributing statistical analyses and interpretation to peer-reviewed biomedical manuscripts.
- Familiarity with cancer research, genetic epidemiology, cancer genetics, or genomic data analysis — desirable but not required.
Mentorship and environment
The fellow will be jointly mentored by faculty in the Department of Data Science and the Division of Cancer Genetics and Prevention, with primary statistical mentorship from Hajime Uno, PhD. The mentoring team will also support the fellow in pursuing career development funding, including NIH K-series awards, when appropriate to the fellow's research interests, eligibility, and long-term career goals.
The fellow will be based in the Department of Data Science at Dana-Farber and will receive an academic appointment at Harvard Medical School or the Harvard T.H. Chan School of Public Health, as appropriate.
This position is particularly well suited for a PhD biostatistician seeking a long-term career in collaborative biostatistical research in academic medicine. The fellow will develop the skills and experience needed to independently lead the statistical components of multidisciplinary research — from study design and analysis planning through interpretation and dissemination — while addressing methodological challenges that arise from substantive scientific questions.