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

Position Summary Leverage advanced machine learning and robust data engineering to create AI-ready datasets, develop predictive models, and deliver analytical solutions within Pharmaceutical Sciences & Translational Safety (PSTS). Work with multidisciplinary teams (toxicologists, PK/PD specialists, in vivo researchers, and safety professionals) to promote improved safety evaluations and facilitate translational research. Key Responsibilities Machine Learning & Modeling: Develop/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/maintain scalable pipelines integrating PSTS-relevant sources (toxicology studies, PK/PD datasets, biomarker readouts, animal study repositories). Transform raw outputs into standardized, analysis-ready, AI-ready datasets using Python, R, and cloud-native services. Collaborate to contribute to harmonized scientific data models. Scientific Domain Integration: Translate study designs into computational requirements with toxicology/DMPK/safety stakeholders. Guide transformations and modeling using mechanism-based toxicology, exposure-response concepts, and in vivo study structures. Improve cross-study comparability via terminologies, metadata practices, and quality checks. Collaborate with PSTS experts, Data Science teams, and platform architects to deliver scalable, high-quality solutions. 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 with ML model development, evaluation, and deployment 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 data, PK time-course datasets). 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 stated): Advanced Analytics, Critical Thinking, Data Analysis, Data Quality, Data Reporting, Data Visualization, Data Savvy, Digital Fluency, Data Privacy Standards, Strategic Thinking, Technical Credibility, Workflow Analysis, Process Improvements, Coaching, Organizing, Econometric Models. Compensation/Benefits Anticipated base pay range: $117,000.00 - $201,250.00 Vacation: 120 hours/year; Sick time: 40 hours/year (CO: 48; WA: 56); Holiday pay (floating holidays): 13 days/year; Work/Personal/Family Time: up to 40 hours/year; Parental Leave: 480 hours within one year; Bereavement Leave: 240 hours immediate family (40 extended family); Caregiver Leave: 80 hours (52-week rolling period); Volunteer Leave: 32 hours/year; Military Spouse Time-Off: 80 hours/year. Application Instructions Candidate interested in Europe-based locations: apply to R-069190. #J-18808-Ljbffr