Postdoctoral Scientist – AI & Machine Learning for Predictive Drug Absorption

Pfizer

Quick summary

Work type
On-site
Location
Groton, CTCambridge, MA
Salary
$64,600–$107,600 / yr
Posted
3 days ago
Closes
Jul 1, 2026

Market check

Salary context

Below market

How this pay compares to similar roles

Similar $209k
This role $86k
$41k most similar roles pay here $281k

This role pays less than 99% of similar roles. Most pay $172,625–$246,150 — the shaded band above. At the midpoint, this role pays about $86k versus about $209k for comparable roles.

Based on 240 similar postings.

Employer

About Pfizer

Pfizer Inc. is one of the world''s largest biopharmaceutical companies, researching, developing, manufacturing, and marketing medicines and vaccines across multiple therapeutic areas including oncology, cardiology, and infectious diseases. Industry: Biopharmaceuticals

Pfizer currently has 34 open roles on FindRole.

Listed pay typically runs $124,400–$207,400 across 34 roles with salary data.

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View all roles at Pfizer

At a glance

TL;DR · Postdoctoral Scientist – AI & Machine Learning for Predictive Drug Absorption

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Pfizer Research & Development seeks a Postdoctoral Scientist to join its Drug Product Design and Supply team, focusing on advancing AI-driven predictive models for oral drug absorption and formulation performance. This role involves designing, training, and evaluating machine learning models using large datasets from various scientific sources, emphasizing scalability and interpretability. Key responsibilities include developing end-to-end ML pipelines, applying diverse ML approaches like neural networks and tree-based methods, and translating model outputs into actionable insights for drug development teams. The ideal candidate holds a PhD in a quantitative field with expertise in Python or R, experience with large datasets, and a track record of research productivity. This position offers mentorship from senior scientists and opportunities for cross-site collaboration within Pfizer’s pharmaceutical sciences division.

Skills

Python scikit-learn PyTorch TensorFlow R machine learning statistical modelling data analysis ML pipelines feature engineering model training validation performance benchmarking biopharmaceutics data clinical pharmacokinetic datasets tree-based methods neural networks surrogate models probabilistic approaches model interpretability explainable AI uncertainty quantification

What you'll do

  • Design, train, and evaluate machine-learning models for predicting oral drug absorption outcomes.
  • Develop end-to-end ML pipelines including data ingestion, feature engineering, model training, validation, and performance benchmarking.
  • Apply and compare various ML approaches such as tree-based methods, neural networks, and probabilistic models for uncertainty-aware prediction.
  • Focus on model interpretability and explainability to link learned patterns with scientifically meaningful drivers.
  • Translate machine learning outputs into actionable insights for drug development teams.
  • Quantify model robustness, generalizability, and uncertainty in data-sparse or extrapolative scenarios.

What we're looking for

  • PhD in Machine Learning, Data Science, or related quantitative field.
  • Less than 2 years post-doctoral experience.
  • At least one first-author publication in high-quality journals.
  • Strong machine learning and statistical modeling expertise.
  • Proficiency in Python/R for data analysis and ML development.
  • Experience with large, heterogeneous datasets in scientific research.
  • Ability to collaborate effectively in multidisciplinary teams.

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