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

Principal Data Scientist – R&D DSDH - Therapeutics Discovery (TD) Responsibilities Develop ML/AI models that support discovery workflows, including target prioritization, multi-omics integration, and mechanistic inference. Apply modern ML approaches (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 to 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 to accelerate dataset preparation, feature engineering, and model lifecycle management across TD. Work with TD scientists to understand key biological/chemical questions and shape computational strategy. Translate sparse, heterogeneous experimental data into insights for hit discovery, mechanism studies, perturbation experiments, and compound optimization. Participate in design, interpretation, and iterative refinement of discovery experiments. Partner cross-functionally (R&D Data Science, IT, platform engineering, therapeutic area groups) to drive AI/ML adoption. Contribute to evaluating 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 Python programming skills (preferred) and experience with ML/scientific 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 real 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, and/or biological assay data. Knowledge of data standards (e.g., FAIR, ontologies, controlled vocabularies) and regulated/quality-governed environments. Strong communication skills; ability to thrive in a matrixed, multidisciplinary environment. Required Skills (from posting) Advanced Analytics Critical Thinking Data Analysis Data Quality Data ReportingData Science Data Visualization Digital Fluency Process Improvements Strategic Thinking Technical Credibility Workflow Analysis Preferred Skills (from posting) Coaching Econometric Models Organizing Data Privacy Standards Data Savvy Benefits (explicitly stated) Vacation: 120 hours per calendar year Sick time: 40 hours per calendar year (CO: 48; WA: 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 immediate family; 40 hours 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 #J-18808-Ljbffr Scorpion Therapeutics