Principal Data Scientist - R&D DSDH - Therapeutics Discovery (TD)

About The Role Develop and apply advanced Machine Learning (ML) and Data Engineering solutions to accelerate scientific innovation across the drug discovery lifecycle. Collaborate with discovery scientists, automation engineers, computational biologists, and platform technology teams to turn complex, multimodal R&D data into actionable insights. Key Responsibilities Machine Learning & Modeling Develop ML/AI models for discovery workflows (target prioritization, multi-omics integration, mechanistic inference). Use modern ML approaches (deep learning, graph learning, foundation models, generative models) on chemical, biological, imaging, and assay datasets. Build/optimize models for real-world R&D use cases with scalability, interpretability, and scientific rigor. Data Engineering & Pipeline Development Design, build, and maintain data pipelines to curate, standardize, and integrate diverse R&D datasets (chemical, biological, multi-omics, imaging, biophysical, automation logs, etc.). Implement best-practice MLOps/DevOps workflows and deploy ML models into production R&D environments. Build tooling to accelerate dataset preparation, feature engineering, and model lifecycle management across TD. Scientific Partnership Partner with TD scientists to understand biological/chemical questions and shape computational strategy. Translate sparse, heterogeneous experimental datasets into insights for hit discovery, mechanism studies, perturbation experiments, and compound optimization. Contribute to design, interpretation, and iterative refinement of discovery experiments. Innovation & Collaboration Partner cross-functionally to drive AI/ML adoption. Evaluate new analytical methods, automation technologies, and data platforms. Champion data quality, documentation, governance, and reproducibility. Qualifications Required 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, etc.). Strong programming skills in Python (preferred) and experience with scientific/ML libraries (PyTorch, TensorFlow, scikit-learn, RDKit, etc.). Data engineering experience: data modeling, workflow orchestration, ETL/ELT pipelines, and cloud computing (AWS, GCP, or Azure). Ability to work directly with experimental scientists to solve R&D challenges. Preferred Pharma/biotech discovery experience (target assessment, phenotypic screening, medicinal chemistry workflows, lab automation). Familiarity with omics, high-content imaging, chemical structure data, or biological assay data. Preferred Knowledge of data standards (e.g., FAIR, ontologies, controlled vocabularies) and work in regulated/quality-governed environments. Strong communication skills in a matrixed, multidisciplinary environment. Required Skills Advanced Analytics, Critical Thinking, Data Analysis, Data Quality, Data Reporting, Data Science, Data Visualization, Digital Fluency, Process Improvements, Strategic Thinking, Technical Credibility, Workflow Analysis Preferred Skills Coaching, Data Privacy Standards, Data Savvy, Econometric Models, Organizing Benefits Vacation – 120 hours per calendar year Sick time – 40 hours per calendar year (Colorado: 48; Washington: 56) 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 birth/adoption/foster care Bereavement Leave – 240 hours for immediate family; 40 hours for extended family 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 Application Instructions Candidate interested in EMEA based locations, please apply to R-069202. #J-18808-Ljbffr