2026 Future Talent Program - Data science Co-op (Proteomics & Genetics)
About this role
Job Description
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.
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.
- Education
- 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.
- Preferred qualifications
- 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.
- Under New York City, Colorado State, Washington State, and California State law, the Company is required to provide a reasonable estimate of the salary range for this job. Final determinations with respect to salary will take into account a number of factors, which may include, but not be limited to the primary work location and the chosen candidate’s relevant skills, experience, and education.