Principal Data Scientist – R&D DSDH - Preclinical Sciences & Translational Safety; PSTS

Principal Data Scientist – R&D DSDH – Preclinical Sciences & Translational Safety (PSTS)
Position Summary

• Use machine learning and data engineering to create AI-ready datasets, build predictive models, and deliver analytics that improve safety evaluations and support translational research within PSTS.

• Develop/deploy ML/AI for safety signal detection, dose selection, PK/PD modeling, toxicology insights, and translational interpretation.
• Build representation‑learning, predictive, and multivariate models across in vivo, in vitro, exposure‑response, and pathology data.
• Create scalable pipelines integrating PSTS‑relevant data sources; transform raw outputs into standardized AI‑ready datasets (Python/R/cloud).
• Partner with toxicology/DMPK/safety SMEs; apply mechanism‑based toxicology and exposure-response concepts; improve cross-study comparability using standardized terminologies, metadata, and quality checks; follow model governance/versioning/validation standards.

• MS or PhD in Data Science/Computational Biology/Toxicology/Pharmacology/Biomedical Engineering/Computer Science (or related).
• 3+ years applying ML/data engineering to scientific/biomedical datasets.
• Proficiency:
  Python and/or R, SQL, and modern data engineering (cloud, workflow orchestration, version control).
• Experience with ML model development/evaluation/deployment; biological/toxicology/PK‑PD/in vivo dataset experience.

• Safety sciences; ADME/DMPK; toxicogenomics/biomarker analytics; scientific data formats; ontologies/semantic tech/knowledge graphs.
• AWS S3/Snowflake/Redshift; SEND/CDISC.

• Advanced analytics; data analysis/visualization; data quality & privacy; critical thinking; strategic/technical credibility; workflow/process improvement.

• Vacation; sick time; holiday pay; work/personal/family time; parental leave; bereavement; caregiver leave; volunteer leave; military spouse time‑off.