Quantitative Safety Scientist

Key Responsibilities

• Design and validate the disproportionality and statistical signal-detection methods powering /signal - PRR, ROR, EBGM (MGPS), Bayesian Confidence Propagation / IC and IC025, observed-vs- expected and time-to-onset analyses.
• Own therapeutic-class-driven assessment logic: comparator selection, indication and channeling confounding control, subgroup and class-effect stratification, and the metrics/calculations that differentiate each certified therapeutic add-on pack (Oncology, Anti-Infective/AMR, Cardiometabolic, CNS, Immunology, Respiratory, Vaccines, Rare Disease).
• Shift the aperture left: partner with discovery, translational and early clinical-development teams to identify candidate safety signals from target biology, mechanism of action, preclinical toxicology and early-phase clinical data - well before a molecule reaches post-marketing surveillance.
• Design a single, reusable molecule-risk-profile framework that carries structured safety evidence forward across the lifecycle - from discovery and molecule identification, through clinical trial safety analytics, into post-marketing signal and risk management - rather than treating each stage as a separate analytic silo.
• Hands-on prototype innovative, exploratory methods - e.g. ML-assisted signal triage, biomarker- informed risk stratification, translational safety scoring - as working proofs of concept in Python/R, ahead of handing validated approaches to engineering for productization.
• Define signal validation, prioritization and management workflows aligned with GVP Module IX, including SMQ-based screening, signal triage criteria and signal lifecycle states.
• Establish methodological guardrails for small databases and tenant-level subsets - minimum reporting thresholds, false-positive control and masking mitigation - so analytics remain valid for SMB and single-sponsor deployments.
• Specify risk-management analytics for /risk: structured benefit-risk frameworks, risk-minimization effectiveness measures, and inputs to RMP / REMS and aggregate reports (PBRER/PSUR).
• Partner with engineering and AI teams to make every metric explainable and auditable - documenting assumptions, formulae and decision logic so outputs withstand EMA / FDA / CDSCO inspection.
• Translate methods into product requirements, acceptance criteria and validation test sets; review outputs as the scientific human-in-the-loop.
• Contribute to scientific positioning - white papers, conference abstracts (e.g. DIA) and methodology documentation.

Qualifications & Requirements

Requirements are mapped to the product capabilities they enable, so candidates can see exactly why

each is needed.

Essential

• Requirement Why it matters / product linkage
• Master's or PhD in Pharmacoepidemiology, Epidemiology, Biostatistics, Pharmacy, Foundation for valid method selection across /signal and Pharmacology or a related quantitative life-science /risk discipline 4–8+ years in pharmacovigilance / drug safety/RWE (preferable), with hands-on signal Direct ownership across the life cycel detection & management experience
• Demonstrated expertise in disproportionality analysis - PRR, ROR, EBGM, BCPNN/IC - and their Core engine of the /signal module correct interpretation
• Working knowledge of GVP Module IX (signal Signal lifecycle, validation & aggregate reporting management) and ICH E2C/E2E (/report)
• Fluency in MedDRA, SMQs and WHODrug Coding-aware screening and class analytics
• Proficiency in Python (pandas, numpy,
• Builds and validates the analytics layer with engineering scipy/statsmodels) and/or R for safety analytics
• Pharmacoepidemiologic judgment - confounding,
• Therapeutic-class assessments in Graph Clinical masking, comparator and threshold selection,