Scientist II, Biomarker Sciences/Translation Sciences

Responsibilities

• Apply computational approaches to uncover biomarkers, mechanisms of drug response and resistance, pharmacodynamic effects, and therapeutic strategies across translational studies.
• Analyze and integrate bulk and single-cell sequencing, spatial transcriptomics, and other molecular data with biomarker, pathology, and efficacy/response outcomes to generate actionable biological hypotheses.
• Develop reproducible analytical workflows and partner on AI-enabled solutions that accelerate exploratory data analysis, visualization, and biological interpretation.
• Partner across Translational Research, Translational Medicine, Discovery, Bioinformatics, and Information Sciences to address key scientific questions across the portfolio.
• Analyze and integrate multimodal molecular and phenotypic datasets from translational studies to identify biomarkers, pharmacodynamic effects, mechanisms of response and resistance, and combination hypotheses.
• Perform cross-study analyses to identify reproducible biological signals and translate computational findings into testable mechanistic and translational hypotheses.
• Partner with experimental scientists on study design and testing of computationally derived biological hypotheses.
• Partner with Biomarker Assays teams to translate insights into actionable biomarker analytes.
• Partner with data science and technology teams to translate scientific needs into scalable analytical and visualization solutions.
• Evaluate and apply emerging AI/ML approaches to accelerate analysis, data integration, and biological insight generation.
• Clearly communicate complex analyses and biological conclusions to multidisciplinary project teams and scientific leadership.

Required Skills, Experience and Education

• PhD in Cancer Biology, Molecular Oncology, Molecular Medicine, Human Genetics, or a related translational biomedical discipline, with 3+ years of relevant postdoctoral and/or industry experience.
• Substantial hands-on experience analyzing and biologically interpreting complex omics datasets, including bulk and single-cell sequencing, spatial transcriptomics, and integrated molecular, phenotypic, and clinical/translational data.
• Strong background in oncology, cancer genomics, and translational biology, with demonstrated ability to independently frame biological questions and translate computational findings into testable hypotheses and drug-development insights.
• Demonstrated experience and proficiency in applying and evaluating machine learning and emerging AI-enabled approaches to biological data analysis.
• Proficiency in R and/or Python for the analysis and interpretation of biological data, with a strong commitment to reproducible and rigorous analytical practices.
• Strong foundation in statistical analysis and experimental design for high-dimensional biological and biomarker data.
• Strong collaboration and scientific communication skills, with the ability to communicate actionable conclusions to multidisciplinary audiences.

Preferred Skills

• Background in oncology drug discovery or development within a biotechnology or pharmaceutical environment.
• Experience analyzing and interpreting longitudinal baseline, on-treatment, and resistance/progression datasets.
• Ability to integrate sequencing data with proteomics, pathology, ctDNA, pharmacology, and/or clinical outcomes.
• Familiarity with cloud-based analytical environments such as Databricks and collaboration on interactive analytical and visualization tools.
• Knowledge of RAS/MAPK biology and RAS-driven cancers.