Principal Data Scientist – R&D DSDH – Therapeutics Discovery

Role Overview

• 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.
• Experience in pharma or biotech discovery , including target assessment, phenotypic screening, medicinal chemistry workflows, and lab automation.
• Familiarity with omics , high‑content imaging , chemical structure data , or biological assay data .
• Knowledge of data standards (e.g., FAIR, ontologies, controlled vocabularies) and working within regulated or quality‑governed environments.
• Strong communication skills and ability to thrive in a matrixed, multidisciplinary environment.

Tech Stack

• AWS
• Azure
• Cloud
• ETL
• Google Cloud Platform
• Python
• PyTorch
• Tensorflow

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 period
• Volunteer Leave – 32 hours per calendar year
• Military Spouse Time-Off – 80 hours per calendar year