Postdoctoral Research Fellow - Biostatistics and Cancer Genetics

Postdoctoral Research Fellow in Biostatistics and Cancer Genetics

Department of Data Science, Dana-Farber Cancer Institute

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. The fellow will also have opportunities to contribute to methodological research motivated by the scientific projects.

Approximately 50% of the fellow’s effort will be devoted to an NIH-funded 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 Dana-Farber Cancer Institute.

Located in Boston and the surrounding communities, Dana-Farber Cancer Institute is a leader in life changing breakthroughs in cancer research and patient care. We are united in our mission of conquering cancer, HIV/AIDS, and related diseases. We strive to create an inclusive, diverse, and equitable environment where we provide compassionate and comprehensive care to patients of all backgrounds, and design programs to promote public health particularly among high-risk and underserved populations.

We conduct groundbreaking research that advances treatment, we educate tomorrow's physician/researchers, and we work with amazing partners, including other Harvard Medical School-affiliated hospitals.

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. Responsibilities will include:
• Conducting statistical analyses of large clinical, genetic, and genomic datasets.
• Performing data derivation, data cleaning, harmonization, quality control, and integration of data from multiple institutions and data sources.
• Developing and validating statistical prediction models.
• Evaluating model discrimination, calibration, clinical utility, and performance across patient subgroups and institutions.
• Conducting simulation studies and computational evaluations.
• Contributing to the development and evaluation of statistical methods motivated by the project.
• Developing reproducible statistical programs and analysis workflows.
• Preparing statistical results, tables, and figures for manuscripts, presentations, and scientific reports.
• Contributing to peer-reviewed manuscripts and other scientific publications.
• Collaborative Cancer Genetics Research — Approximately 50% Effort
• The fellow will also serve as a collaborative biostatistician for research conducted within the DFCI cancer genetics program. The fellow will collaborate with clinical investigators, geneticists, genetic counselors, and other research team members on clinical, translational, and observational studies in cancer genetics, hereditary cancer, and cancer prevention.

Responsibilities will include:

• Providing statistical input on study design and research questions.
• Developing statistical analysis plans.
• Performing data management, data derivation, and statistical analyses.
• Selecting and applying appropriate statistical methods for clinical and biomedical research.
• Interpreting statistical 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.
• Managing multiple collaborative projects and working effectively with investigators and research staff.

Qualifications

• Required
• PhD or equivalent doctoral degree in Biostatistics at the time of appointment.
• Strong training in applied biostatistical methods.
• Experience with regression modeling and other statistical 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 statistical code and analysis workflows .
• Experience with data management, data derivation, and statistical analysis of clinical or biomedical datasets.
• Ability to independently conduct statistical analyses from preparation of analysis-ready datasets through interpretation and presentation of results.
• Strong written and verbal communication skills.
• Interest in collaborative biomedical research and multidisciplinary teamwork.
• 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 is desirable but not required.
• Mentorship
• The fellow will be jointly mentored by faculty members 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.
• Research and Career Development Environment
• The fellow will be based in the Department of Data Science at Dana-Farber Cancer Institute and will work in a highly collaborative academic research environment. The fellow will also 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.

The fellow will also have opportunities to address methodological challenges arising from substantive scientific questions and, when appropriate, to develop new statistical approaches. Over time, the fellow is expected to grow into a biostatistical researcher who can lead collaborative research while contributing methodological innovation to important problems in cancer research.

Appointment Terms

The initial appointment is for one year, with the possibility of annual renewal based on satisfactory performance. Grant funding supporting the position is anticipated to be available for up to five years.

At Dana-Farber Cancer Institute, we work every day to create an innovative, caring, and inclusive environment where every patient, family, and staff member feels they belong. As relentless as we are in our mission to reduce the burden of cancer for all, we are committed to having faculty and staff who offer multifaceted experiences. Cancer knows no boundaries and when it comes to hiring the most dedicated and compassionate professionals, neither do we. If working in this kind of organization inspires you, we encourage you to apply.

EEO Poster

. Pay Transparency Statement

The hiring range is based on market pay structures, with individual salaries determined by factors such as business needs, market conditions, internal equity, and based on the candidate’s relevant experience, skills and qualifications.

For union positions, the pay range is determined by the Collective Bargaining Agreement (CBA).

$72,000.00 - $76,385.00