Manager, Data Scientist

As a member of our AI/ML, Quantitative & Digital Sciences (AQDS) team, you will:

• Generate insights using advanced AI/ML techniques from Pfizer’s internal and external data, with the focus on clinical development. 

• Partner with clinical statisticians, data scientists, clinical development leads, and evidence teams to collaborate effectively on the application of AI/ML techniques to impact Pfizer’s portfolio, under the guidance of the AQDS function lead.

• Collaborate with Pfizer teams to support decision making and implementation of AI/ML models (e.g., Generative AI, Predictive AI, and Knowledge Graphs). 

• Work with a team of data scientists to develop, test, and validate AI/ML and Generative AI tools for use across multiple therapeutic areas; build and deploy services via FastAPI and orchestration on platforms such as Dataiku/Snowflake. 

• Work with internal and external teams to bring the latest AI/ML solutions and technologies to Pfizer. 

Position Responsibilities

• Collaborate with key therapy areas (e.g., Immunology & Inflammation, Oncology, Internal Medicine) to identify scientific problems, map data sources, select and implement AI/ML methods (incl. NLP/RAG, knowledge graphs, and document processing), and convey insights to clinical and operational teams.

• Review and supervise the full lifecycle, from data collection/aggregation through model building and deployment, to ensure robust performance for predictive, classification, and generative applications via well‑developed analysis and evaluation plans (e.g., DeepEval, TruLens, RAGAS, LangSmith, Bayesian approaches).

• Ensure Pfizer methodology remains current through knowledge‑sharing on new tools and vendors in AI/ML, including agent frameworks (e.g., LangChain/LangGraph, AutoGen, Crew AI, Pydantic AI) and reinforcement learning for foundational models (e.g., GRPO, PPO, RLVR). 

• Translate clinical data and analytics needs and priorities into tactical projects for AQDS to lead and advance clinical development.

Basic Qualifications

• Advanced degree (MEng/MSc or PhD) in computer science, Computational Biology, Bioinformatics, Biostatistics, Applied Mathematics, Scientific Machine Learning, Physics, Operations Research or Biomedical Engineering and a minimum of 3 years related data/analytics experience in a pharma environment or biotech/tech industry. 

• Experience developing and applying AI/ML to complex, multi‑dimensional problems, including clinical data and document analysis. 

• Clear understanding of the opportunities and challenges presented with applying AI/ML to business problems within the life sciences industry.

• Proven ability to translate customer needs into AI/ML solutions.

• Strong programming skills in Python (e.g., FastAPI, LangChain/LangGraph, Pydantic) and SQL; familiarity with modern data/ML tooling (e.g., Dataiku, Snowflake). 

• Experience with cloud technologies (preferably AWS, Azure) and MLOps (GitHub Actions, Docker, MLflow).

• Experience with Large Language Models (LLMs), including prompting, retrieval, evaluation, and fine‑tuning.

• Excellent written and verbal communication skills.

Preferred Qualifications

• Hands-on experience shepherding early or late-stage clinical development projects from study design through submission.

• Experience applying advanced analytics and AI/ML to clinical trials.

• Experience with LLM training/tuning, including reinforcement learning approaches (GRPO, PPO, RLVR) and evaluation frameworks. 

• Demonstrated success leading small, cross‑functional teams and working across time zones.

• Evidence of external impact (e.g., publications, talks) and collaboration with internal/external partners.

  

Please apply by sending your CV and a motivational letter in English

  
Work Location Assignment: Hybrid

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