Associate Director, Data Science, Computational Oncology

Job Description

Associate Director of Data Science, Computational Oncology (R4)

Translational Genome Analytics (TGA) / Data, AI and Genome Sciences (DAGS)

Location: Boston, MA (hybrid)

Our company is a global health care leader committed to being the world's premier, most research-intensive biopharmaceutical company. Our Research Laboratories take our leading discovery capabilities and world-class small molecule and biologics research and development expertise to create breakthrough science that radically changes how we approach serious diseases.

The Data, AI and Genome Sciences (DAGS) department seeks a talented data scientist with a strong quantitative and statistical foundation to join our Translational Genome Analytics (TGA) team in Boston, MA. In this role, you will bring rigorous statistics, machine learning, and modern AI to large, complex multi-omics and functional genomics datasets to understand the molecular basis of cancer and to nominate targets and biomarkers across our research portfolio. You will work closely with computational, AI and ML, and experimental colleagues to turn data into decisions that move programs forward. We are especially interested in scientists with a strong oncology and cancer genomics background who can also analyze functional genomics screens, so you can support target nomination and the broader discovery portfolio from both directions.

You are an independent scientist who takes an open-ended question and carries it through to a clear answer, and who thrives in a highly collaborative discovery environment.

In This Exciting Role, You Will

• Analyze large-scale oncology datasets, including bulk RNA-seq, whole-exome and whole-genome sequencing (WES/WGS), and single-cell RNA-seq (scRNA-seq), to understand disease biology and the mechanisms of disease progression and drug action.
• Analyze functional genomics and perturbational screens, including pooled and arrayed CRISPR screens and Perturb-seq, from QC and hit calling through biological interpretation, to establish target dependency and mechanism for programs across therapeutic areas.
• Nominate and prioritize drug combinations for both oncology and immunology disease areas by weighing expression profiling, disease biology, mechanistic and functional-genomics evidence, and the competitive landscape together, and bring that evidence to program teams across the portfolio.
• Apply rigorous statistics and integrate multi-omics and functional genomics data with prior biological knowledge, using network-based and machine learning approaches, to reach robust conclusions and a coherent picture of target and pathway biology.
• Apply deep learning and single-cell foundation models to problems such as cell-type deconvolution, batch integration, patient stratification, and perturbation-response prediction.
• Use modern AI, including LLM-based agents and retrieval over internal data, to accelerate evidence synthesis and analysis, and help bring these tools into routine, well-validated use.
• Draw on human and real-world data to inform and reverse-translate discovery hypotheses, connecting preclinical findings to patient biology.
• Collaborate closely across disciplines, including experimental scientists, AI and ML and data science teams, software engineers, and program teams, and set a high bar for reproducible, well-documented research that supports Discovery Oncology.

Minimum Requirements

• A PhD in computational biology, bioinformatics, biostatistics, genetics/genomics, computational biology, mathematics, computer science, biophysics, computational chemistry or a related quantitative STEM discipline and 4+ years of relevant experience; MS and 8+ years of relevant experience; OR BS and 12+ years of relevant experience.
• A passion for solving problems in oncology and cancer genomics through computational methods, with a consistent focus on detail and execution.
• Deep experience analyzing and biologically interpreting large-scale NGS datasets (bulk RNA-seq, WES/WGS, scRNA-seq), including experimental design, QC, and building analyses where no standard pipeline exists, and integrating multiple omics layers with prior biological knowledge.
• A strong statistical foundation, spanning hypothesis testing, regression and mixed models, survival analysis, and dimensionality reduction, with sound judgment about confounding, batch effects, and multiple comparisons, and a track record of applying machine learning to biological data with judgment about where it adds value and where simpler methods suffice.
• Strong technical skills, including R or Python, version control (Git), and Linux, with hands-on experience across cloud and data platforms such as AWS (S3), Nextflow, Databricks, and Posit.
• Familiarity with major cancer genomics resources, such as TCGA, GTEx, CCLE, CPTAC, and DepMap.
• A collaborative working style and excellent oral and written communication skills.

Preferred Experience and Skills

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