Computational Lead Scientist - Translational Immunogenomics Lab

Dana-Farber Cancer Institute

Job Description

Located in Boston and the surrounding communities, Dana-Farber Cancer Institute brings together world renowned clinicians, innovative researchers and dedicated professionals, allies in the common mission of conquering cancer, HIV/AIDS and related diseases. Combining extremely talented people with the best technologies in a genuinely positive environment, we provide compassionate and comprehensive care to patients of all ages; we conduct research that advances treatment; we educate tomorrow's physician/researchers; we reach out to underserved members of our community; and we work with amazing partners, including other Harvard Medical School-affiliated hospitals.

The Translational Immunogenomics Lab (TIGL) within DFCI uses state-of-the-art genomic technologies to assess the response of the immune system to cancer and therapy. TIGL evaluates the latest developments in the field, implements the optimal methods for the task at hand, and invents methods when current approaches are inadequate. The overall goal of TIGL is to use genomic technology to evaluate biomarkers of response and resistance to immunotherapy. One particular focus is discovering patient-specific neoantigens, understanding the details and consequences of interactions between neoantigens and T cells, and applying this knowledge to support neoantigen immunization therapy.


We are seeking a highly motivated and enthusiastic computational scientist to join the Translational Immunogenomics Lab (TIGL) and lead a team of bioinformatics analysts working on immune-related projects in cancer. The primary focus of the team is the analysis of data from high-throughput biological profiling technologies including DNA and RNA sequencing, both bulk and single-cell, and mass spectrometry.

Examples of projects include:
Creation and maintenance of computational pipelines using publicly available and locally developed tools for analysis of patient data from immunotherapeutic trials.
Computational and statistical analysis of cancer genome and transcriptome sequencing results.
Prediction of neoantigens from genomic mutation data.
Combined analysis of data from repositories such as the The Cancer Genome Atlas project (TCGA) and Gene Expression Omnibus (GEO) with data generated in-house for generation of novel biological insights.
Creation of pipelines for interpreting data from single-cell RNA-Seq experiments.
Packaging of tools for sharing with collaborators and the scientific community.
The candidate is expected to work with experimental scientists to develop the overall scientific strategy for TIGL, including novel approaches.


Education and Experience
Ph.D. in bioinformatics, computational biology, computer science, mathematics, physics, or related discipline (or equivalent research/industrial experience) with 5+ years post-degree experience.

Ability to lead and manage a project team to accomplish a complex set of goals.
Ability to apply statistical and machine learning techniques to solve big data problems.
Experience developing algorithms and proficiency in coding (C/C++, Perl, Python, JAVA, Scala, or equivalent).
Significant experience in data analysis languages/environments (R, MATLAB, or equivalent).
Ability to synthesize information from other disciplines to achieve broad-based goals.
Familiarity with best developmental practices for delivering working, tested software from a team of computational scientists.
Must have excellent communication skills (written and verbal) and be able to work with a wide variety of faculty and staff in a highly collaborative and intellectually challenging environment.

Dana-Farber Cancer Institute is an equal opportunity employer and affirms the right of every qualified applicant to receive consideration for employment without regard to race, color, religion, sex, gender identity or expression, national origin, sexual orientation, genetic information, disability, age, ancestry, military service, protected veteran status, or other groups as protected by law.

Employment Type

full time