Develop ML/AI models that support discovery workflows, including target prioritization, multi‑omics integration, and mechanistic inference.
Apply modern ML approaches (e.g., deep learning, graph learning, foundation models, generative models) to chemical, biological, imaging, and assay datasets.
Build and optimize models for real‑world R&D use cases, ensuring scalability, interpretability, and scientific rigor.
Design, build, and maintain robust data pipelines that curate, standardize, and integrate diverse R&D datasets (chemical, biological, multi‑omics, imaging, biophysical, automation logs, etc.).
Partner with platform teams to implement best‑practice MLOps/DevOps workflows and deploy ML models into production R&D environments.
Develop tooling that accelerates dataset preparation, feature engineering, and model lifecycle management across TD.
Work hand‑in‑hand with TD scientists to understand key biological and chemical questions and shape computational strategy accordingly.
Translate sparse, heterogeneous experimental datasets into insights that guide decision‑making in hit discovery, mechanism studies, perturbation experiments, and compound optimization.
Participate in design, interpretation, and iterative refinement of discovery experiments.
Partner with cross-functional teams in R&D Data Science, IT, platform engineering, and therapeutic area groups to drive AI/ML adoption.
Contribute to evaluating new analytical methods, automation technologies, and data platforms supporting next‑generation discovery science.
Champion high standards for data quality, documentation, governance, and reproducibility.

Requirements

Master’s or Ph.D. in Computational Biology, Bioinformatics, Data Science, Chemistry, Chemical Biology, Biomedical Engineering, Computer Science, or related field.
Experience applying ML/AI in scientific domains (drug discovery, biology, chemistry, systems biology, imaging, or related areas).
Strong programming skills in Python (preferred) and experience with scientific/ML libraries (PyTorch, TensorFlow, scikit‑learn, RDKit, etc.).
Practical experience with data engineering , including data modeling, workflow orchestration, ETL/ELT pipelines, and cloud computing environments (AWS, GCP, or Azure).
Ability to work directly with experimental scientists to solve real R&D challenges.

Benefits

Vacation –120 hours per calendar year
Sick time - 40 hours per calendar year; for employees who reside in the State of Colorado –48 hours per calendar year; for employees who reside in the State of Washington –56 hours per calendar year
Holiday pay, including Floating Holidays –13 days per calendar year
Work, Personal and Family Time - up to 40 hours per calendar year
Parental Leave – 480 hours within one year of the birth/adoption/foster care of a child
Bereavement Leave – 240 hours for an immediate family member: 40 hours for an extended family member per calendar year
Caregiver Leave – 80 hours in a 52-week rolling period10 days
Volunteer Leave – 32 hours per calendar year
Military Spouse Time-Off – 80 hours per calendar year

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

ML modelsAI modelsdeep learninggraph learningfoundation modelsgenerative modelsdata pipelinesfeature engineeringdata modelingworkflow orchestration

Soft Skills

collaborationproblem-solvingcommunicationinterdisciplinary teamworkdecision-makingadaptabilitycritical thinkingscientific rigordata quality advocacydocumentation

Certifications

Master’s degreePh.D.