Senior Scientist - Translational Safety & Risk Sciences (TSRS)
Lifelancer · United States · US
Clinical Data Science
Remote
physician
Pharmacist
Dentist
Demand82
Career growth78
Healthcare fit60
Remote potential55
5-year outlook
Likely competitive large-pharma R&D compensation with upward movement toward principal scientist or data-science leadership; exact salary is not stated and should be verified.
Clinical license
Not required
Applicant reach
narrow
Seniority
senior
Employment type
full-time
Date found
2026-10-20
Intelligence details
Flexible work details
- Engagement type
- Permanent
- Hours
- Full-time
- Eligible countries
- United States
- Profession fit
- physician, Pharmacist, Dentist
- Rate period
- Annual
Listing via Himalayas.
Eligibility, licensing, and location restrictions can change. Always confirm details on the source posting before applying.
Why this is worth applying to
Worth applying for clinicians with strong data science/engineering and AI/ML experience who want pharma translational safety; most clinicians without that technical portfolio will find the bar too high.
Who should apply
PhD/PharmD or clinically trained scientists with bioinformatics, data engineering, dashboarding, and AI/ML experience; translational safety, nonclinical safety, or pharma R&D data professionals.
CV angle
Reframe clinical and research experience around translational safety domain knowledge, data standardization, metadata/ontology, pipeline/database development, dashboarding, and AI/ML applications; quantify cross-functional projects and data governance/reproducibility work without inventing technical experience.
Skills to highlight
- Translational safety
- Nonclinical safety
- Data infrastructure
- Databricks
- Spotfire/data visualization
- Metadata standards and ontology
- AI/ML predictive modeling
- Data governance and reproducibility
Role summary
Senior pharma R&D scientist role building data infrastructure, databases, dashboards, and AI/ML-ready data ecosystems for translational safety and New Approach Methodologies (NAMs), collaborating with scientific, IT, data engineering, and AI/ML teams.
We publish our own intelligence summary, never the full job description. Always confirm details with the employer when you apply.