Senior Data Scientist - Multiomics and Population Cohorts

Senior Data Scientist - Multiomics and Population Cohorts

Michigan Medicine improves the health of patients, populations and communities through excellence in education, patient care, community service, research and technology development, and through leadership activities in Michigan, nationally and internationally.

Our mission is guided by our Strategic Principles and has three critical components; patient care, education and research that together enhance our contribution to society.

The Division of Cardiovascular Medicine at the University of Michigan is expanding a nationally and internationally funded research program at the cutting edge of cardiovascular medicine. Led by Drs. Venkatesh Murthy and Sascha Goonewardena - whose work has appeared in leading journals including NEJM AI, JAMA, and Circulation - the program fuses artificial intelligence, advanced cardiac imaging, multiomics (proteomics, metabolomics, and genomics), and cardiometabolic disease biology to develop precision diagnostics and identify novel therapeutic approaches, with a particular focus on coronary microvascular disease and other cardiovascular conditions that disproportionately affect women and remain poorly served by existing diagnostic tools.

This is a rare opportunity to join a high-impact, well-funded program doing science that matters.

This position serves as the primary multiomics computational scientist for the program. The incumbent will build and maintain proteomics and multi-omic analysis pipelines, deploy AI model inferences at scale in large population biobanks, and apply causal and genetic inference methods to identify biological mechanisms underlying cardiometabolic disease. This is an infrastructure and pipeline-focused role: the incumbent constructs the computational frameworks and cross-cohort data systems that make population-scale discovery possible, working closely with the postdoctoral scientist who leads the hypothesis-driven analyses and manuscript development.

This is one of two senior data scientist roles; the two positions serve complementary, non-overlapping functions.

• Build, implement, and maintain proteomics and multi-omic analysis pipelines integrating high-throughput proteomic data with metabolomic, genomic, and clinical datasets from large prospective cohort studies
• Deploy AI models to biobank datasets
• Build and maintain cross-cohort data harmonization and proteomic cross-walk tools across derivation and validation datasets
• Perform causal and genetic inference analyses (including Mendelian randomization and mediation analysis) to identify disease mechanisms
• Evaluate pipeline and signature performance; prepare written and code-based analytical reports (Python or R)
• Contribute to manuscripts, grant applications, and presentations
• Coordinate data sharing and computational workflows with collaborative network partners and other research partners
• Other duties as assigned

• Masters or doctoral degree in bioinformatics, computational biology, biostatistics, systems biology, or a closely related field
• Demonstrated experience analyzing large-scale proteomic datasets from high-throughput platforms (Olink or Soma Scan)
• Proficiency in R and/or Python for statistical analysis and pipeline development
• Experience with multi-omic data integration combining at least two of: proteomics, metabolomics, genomics, transcriptomics
• Familiarity with statistical methods for high-dimensional biological data (dimensionality reduction, regularized regression, survival analysis
• Strong organizational skills and attention to detail
• Ability to prepare and present written and code-based (Python or R) analytical reports
• Strong scientific writing skills; ability to contribute to manuscripts and grant applications

• Experience with large prospective cohort or biobank datasets (e.g., CARDIA, MESA, Framingham Heart Study, UK Biobank, or similar)
• Experience with mediation analysis, causal inference, or Mendelian randomization methods in an omics context
• Background in cardiovascular biology, vascular biology, or cardiometabolic disease
• Experience with cloud or HPC computing environments, including Slurm-based job scheduling
• Familiarity with SQL or PostgreSQL for data querying and management
• Track record of peer-reviewed publications as a computational contributor to biomedical research
• Familiarity with endothelial biology, inflammation, or microvascular disease
• Experience with Git and reproducible research practices ? Experience building reproducible pipelines using workflow managers (Snakemake, Nextflow, or equivalent)

Michigan Medicine is one of the largest health care complexes in the world and has been the site of many groundbreaking medical and technological advancements since the opening of the U-M Medical School in 1850. Michigan Medicine is comprised of over 30,000employees and our vision is to attract, inspire, and develop outstanding people in medicine,…