Senior Expert Data Scientist, Human Genetics

Novartis•Cambridge, MA

1d•$126,000 - $234,000•Onsite

About The Position

Novartis is seeking a computational scientist to join the Cardiovascular and Metabolic Disease (CVM) research organization within Biomedical Research in Cambridge, Massachusetts. The role applies human genetics, statistical genetics, and genomic data science to support therapeutic target discovery and patient-selection strategies across CVM diseases. The scientist will work with geneticists, computational biologists, disease-area scientists, translational researchers, and clinical development colleagues to generate actionable insights from human genetic and genomic data and contribute to genetically informed precision medicine and drug discovery programs.

Requirements

PhD with 2–3+ years, or MS with 6+ years, of industry experience in human genetics, statistical genetics, computational biology, bioinformatics, epidemiology, biostatistics, or a related field.
Demonstrated experience analyzing whole-exome or whole-genome sequencing data, including variant quality control, annotation, filtering, association testing, and interpretation.
Strong understanding of human genetics and statistical genetics principles, including rare-variant analysis and genotype-phenotype associations.
Familiarity with genome-wide association studies, phenome-wide association studies, polygenic risk scores, Mendelian randomization, or functional genomics.
Experience integrating genomic data with clinical, molecular, or multi-omic datasets.
Programming and data analysis expertise in Python or R, and proficiency in Linux or Unix-based computing environments.
Knowledge of workflow management systems (Git), collaborative coding, and reproducible research practices.
Ability to work with large-scale genomic datasets and use appropriate statistical and computational methods to derive biologically meaningful conclusions.
Strong scientific communication, collaboration, and problem-solving skills.

Nice To Haves

Postdoctoral research experience.
Experience applying human genetic evidence to target discovery, target validation, or drug development.

Responsibilities

Analyze and interpret whole-exome and whole-genome sequencing data to identify variants, genes, and biological pathways associated with cardiovascular and metabolic diseases.
Develop and apply statistical genetics and bioinformatics methods for variant annotation, association analysis, burden testing, rare-variant analysis, causal inference, and gene prioritization.
Support identification, evaluation, and validation of therapeutic targets using human genetic evidence and complementary genomic data sources.
Contribute to patient-selection and stratification strategies for research and clinical programs, including identifying genetically defined patient subgroups.
Integrate human genetics results with transcriptomic, proteomic, functional genomics, clinical, and other relevant data to develop disease and target insights.
Collaborate with cross-functional project teams to define analytical strategies, interpret findings, and translate results into scientific recommendations.
Develop reproducible, well-documented analytical workflows and contribute to genomic data analysis best practices.
Communicate results through presentations, written reports, scientific manuscripts, and internal project updates.
Stay current with advances in human genetics, statistical genetics, genomic medicine, and computational methods relevant to drug discovery and development.