Upsilon Global are seeking a freelance Data Scientist/Data Engineer on behalf of a renowned pharmaceutical developer.
The role requires strong python skills, real technical skills in RWE (real world evidence) as well as a solid awareness and knowledge of machine learning and AI. Experience with pharmacoepidemiology is essential, with experience working in big pharma being highly advantageous.
This role requires someone with a proven ability to deliver complex, cross-functional projects, working with large-scale datasets and modern tech stacks, strong communication and stakeholder management skills and, leadership qualities in a global, matrix environment.
Quick info
* Role: Freelance Data Scientist/Data Engineer
* FTE: 1.0 fte
* Duration: Initial 12 month contract
* Start: ASAP
* Client: Global Pharma
Job Description – RWE Evidence Generation, Causal Modelling & AI/ML Clinical Development Lead Delivery of RWE Evidence Generation Projects
The role is responsible for the design, delivery and scientific leadership of Real‑World Evidence (RWE) generation projects across relevant therapeutic areas, supporting clinical development from very early stages (M0, M1, M2) to Phase I, Phase II and Phase III.
Key responsibilities include:
* Partnering with RWE Portfolio Heads, GPTs and cross‑functional stakeholders to define project MVPs, ensuring user needs are translated into actionable and high‑impact deliverables.
* Producing project business cases, reporting progress and defining system and data dependencies across the RWE portfolio.
Contribution to RWE Portfolio & Platforms Strategy
* Shape RWE platform strategy by supervising the development lifecycle of evidence generation projects, ensuring alignment with clinical development timelines and TA needs.
* Guarantee access to high‑quality RWD sources (clinical, EMR, claims, registries, genomic, synthetic datasets) through close collaboration with internal and external partners.
* Represent the organization in external scientific and regulatory interactions (FDA, EMA) to support integration of RWE into early‑phase and late‑phase clinical development.
Regulatory & Policy Engagement
* Work with regulatory and policy teams to develop, present and defend RWE packages supporting clinical development, early signal generation, trial optimization and label expansion.
* Ensure appropriate dissemination of analysis outputs to guide clinical strategy, protocol design and evidence generation planning.
Integrated Expertise – RWE, RWD & Clinical Development (M0‑PH3)
The ideal candidate brings strong experience using RWE and RWD to support clinical development across the full lifecycle, with demonstrated impact at: M0/M1/M2/PhI/PhII/PhIII.
AI/ML and Causal Modelling Expertise
The candidate should demonstrate a strong record of applying modern machine learning, deep learning and causal inference techniques to clinical and RWE datasets, including:
* Deep learning pipelines for HTS, omics, biomarker prediction or multimodal datasets.
* Longitudinal forecasting and disease progression modelling.
* Development of R/Python packages, modelling frameworks and decision‑science tools used by RWE and clinical teams.
* Ability to integrate mechanistic, statistical and ML models to answer key clinical development questions, optimize study design and accelerate asset progression.
Big Pharma Clinical & RWE Experience
A strong plus is prior experience in major pharmaceutical companies (e.g., AstraZeneca, BMS, GSK, Boehringer Ingelheim or equivalent), combining:
* Experience working in global matrixed structures
* Familiarity with regulatory science and agency expectations
Experience
* Prior experience in RWE/RWD, pharmacoepidemiology or clinical development in industry is required.
* Proven ability to deliver complex, cross‑functional projects.
* Experience with large‑scale datasets and modern tech stacks.
Soft Skills
* Strong conceptual and integrative thinking.
* Excellent communication and stakeholder management.
* Leadership in global and matrixed environments.
* Innovative and comfortable navigating emerging scientific methods.
Technical Skills
* RWE, causal inference, epidemiology, biostatistics.
* Machine learning, deep learning, and modern data science tooling.
* Solid understanding of relevant TAs (I&I, Neuroscience, oncology, Rare Diseases).
* Knowledge of regulatory frameworks underpinning evidence generation and clinical decision‑making.
Education
* PhD in Health Sciences or related field, with strong quantitative training.
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