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.

• 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.

• 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.

• 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.

• 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.

• 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).

• Advanced Analytics
• Data Analysis
• Data Quality
• Data Reporting
• Data Science
• Data Visualization
• Critical Thinking
• Strategic Thinking
• Technical Credibility

• Coaching
• Data Privacy Standards
• Data Savvy
• Digital Fluency
• Econometric Models
• Organizing
• Process Improvements
• Workflow Analysis

• 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

Candidate interested in Europe-based locations: apply to R-069190.