2026 Future Talent Program - Data Scientist, Causal Network - Co-op

MSD•Cambridge, MA

1d•Hybrid

About The Position

The Future Talent Program features Cooperative (Co-op) education that lasts up to 6 months and will include one or more projects. These opportunities in our Research and Development Division can provide you with great development and a chance to see if we are the right company for your long-term goals. The Precision Genetics group within Data, AI & Genome Sciences (DAGS) is seeking a self-motivated Co-op student (up to 6 months) to support large-scale proteomics, genetics, and multi-omics projects. You will work with scientists to develop reproducible analytical frameworks that integrate biobank-scale proteomic and other omics data to accelerate biomarker and target discovery. This role offers hands-on experience with state-of-the-art data analysis and opportunities to contribute to method development.

Requirements

Currently enrolled in at least a bachelor’s program in statistics, biostatistics, bioinformatics, computational biology, data science, computer science, or a related field.
Graduate students (MS/PhD) are strongly encouraged to apply.
Available to work full-time for 6 months.

Nice To Haves

Experience analyzing large-scale omics datasets (proteomics, transcriptomics, genetics etc.).
Familiarity with statistical genetics methods (GWAS, QTL, PRS etc.).
Experience with biobank or consortium datasets and secure research environments (Databricks, DNAnexus, or similar).
Proficient in R and/or Python; experience developing reproducible workflows and using version control.
Experience with cloud or HPC environments and scalable/parallel workflow design.
Knowledge of network/graphical models or ML methods applied to genomics.
Strong written and oral communication skills.

Responsibilities

Curate and preprocess biobank-scale proteomics and other omics datasets (e.g., UK Biobank) according to project needs.
Integrate proteomics, genetics, and clinical data to perform association analyses and stratified analyses for target and biomarker discovery.
Build and compare protein- and pathway-level causal networks across cohorts and disease states to interpret molecular relationships.
Explore and develop statistical and AI/ML approaches leveraging multi-omics data for biomarker discovery and patient stratification.
Collaborate with cross-functional teams (computational biology, statistical genetics, AI/ML, and therapeutic-area experts).
Contribute to scalable, reproducible analysis pipelines and documentation.