Senior/Principal Scientist - Translational Statistics - Decision Science

Servier

Gif-sur-Yvette, France

Position Overview:

As a key member of the Translational Statistics team, you will be responsible for generating quantitative evidence to support development strategies across the translational continuum. Operating at the interface of Translational Medicine, Clinical Development, and Biometrics, you will ensure that biomarker and translational research data are converted into actionable conclusions to inform strategic development choices, from preclinical research to early clinical studies.

Main Responsibilities:

Translate biomarker and translational insights into decision-relevant evidence supporting development strategy.
Integrate preclinical, RWD, and early clinical evidence to inform PoC positioning and Go/No-Go considerations.
Quantify the credibility of biomarker-based enrichment strategies in data-limited PoC settings.
Develop Bayesian frameworks to quantify probability of treatment benefit across doses or biomarker-defined populations.
Apply simulation approaches and external evidence borrowing strategies to strengthen inference and inform development scenarios.
Leverage virtual patient frameworks and modeling outputs to support preclinical-to-clinical transition decisions.

Required Skills and Experience:

Educational & Professional Background: PhD in Biostatistics, Statistics, Biomathematics or related quantitative discipline. MSc candidates with relevant experience could also be considered. 5+ years of experience in pharmaceutical R&D with exposure to translational research, biomarker analyses or early clinical development.
Technical Expertise (Hard Skills):
- Solid understanding of early clinical development processes and decision frameworks.
- Statistical reasoning in small sample and high-uncertainty settings.
- Experience applying Bayesian approaches, including futility framework.
- Simulation of development scenarios and clinical decision rules.
- Experience with external data borrowing (synthetic data generated by generative models would be a plus).
- Familiarity with PK/PD or QSP modeling principles would be a plus.
Language: Scientific level English.