Principal Data Scientist - R&D DSDH - Preclinical Sciences & Translational Safety (PSTS)

Principal Data Scientist – R&D DSDH - Preclinical Sciences & Translational Safety (PSTS) Position Summary Leverage advanced machine learning and robust data engineering to generate actionable insights within Pharmaceutical Sciences & Translational Safety (PSTS). Create AI-ready datasets, develop predictive models, and deliver analytical solutions to improve safety evaluations and facilitate translational research. Key Responsibilities Machine Learning & Modeling Develop and deploy ML/AI models for safety signal detection, dose selection, PK/PD modeling, toxicology insights, and translational interpretation. Use representation learning, predictive modeling, and multivariate analytics across in vivo studies, in vitro assays, exposure-response data, and pathology information. Partner with scientific SMEs to align modeling strategies with PSTS decision points. Apply model governance, versioning, and validation standards. Data Engineering & Pipeline Development Build and maintain scalable pipelines integrating toxicology studies, PK/PD datasets, biomarker readouts, and animal study repositories. Transform raw experimental outputs into standardized, analysis-ready, AI-ready datasets using Python, R, and cloud-native services. Collaborate on harmonized scientific data models with data engineering and ontology teams. Scientific Domain Integration Translate study designs into computational requirements with toxicology, DMPK, and safety stakeholders. Apply mechanism-based toxicology, exposure-response concepts, and in vivo study structures to guide data transformations and modeling. Improve cross-study comparability using standardized terminologies, metadata practices, and quality checks. Ensure high-quality, scalable data solutions with PSTS functional experts, Data Science teams, and platform architects. Qualifications Required MS or PhD in Data Science, Computational Biology, Toxicology, Pharmacology, Biomedical Engineering, Computer Science, or related field. 3+ years applying machine learning and/or data engineering to scientific/biomedical datasets. Proficiency with Python and/or R, SQL, and modern data engineering tooling (cloud computing, workflow orchestration, version control). Experience developing, evaluating, and deploying ML model pipelines. Experience with biological, toxicology, PK/PD, or in vivo datasets. Preferred Safety sciences experience; ADME/DMPK, toxicogenomics, or biomarker analytics. Familiarity with scientific data formats (assay outputs, histopathology, PK time-course). Exposure to ontologies/semantic technologies/knowledge graph integration. Cloud data architecture experience (AWS S3, Snowflake, Redshift). Understanding of regulatory data standards (SEND, CDISC). Required/Preferred Skills (as listed) Required Skills: Advanced Analytics Data Analysis Data Quality Data Reporting Data Science Data Visualization Critical Thinking Strategic Thinking Technical Credibility Preferred Skills: Coaching Data Privacy Standards Data Savvy Digital Fluency Econometric Models Organizing Process Improvements Workflow Analysis Benefits (explicitly stated) Vacation: 120 hours/calendar year Sick time: 40 hours/calendar year (CO: 48; WA: 56) Holiday pay (Floating Holidays): 13 days/calendar year Work, Personal and Family Time: up to 40 hours/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/calendar year Military Spouse Time-Off: 80 hours/calendar year Application Instructions Candidate interested in Europe-based locations: apply to R-069190. #J-18808-Ljbffr