Senior Principal Machine Learning Scientist

Pfizer

. Develop, extend, and rigorously evaluate advanced AI methods that generate scientific insights, accelerate experimentation, and enable better decision-making across the drug discovery and development lifecycle .

Posted 11/6/2026full-timeUnited StatesSenior💰 $139,100 - $231,900 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in developing and evaluating advanced AI methods for drug discovery, with a strong focus on machine learning methodologies, experimental design, and translating research insights into actionable outcomes. Proficient in collaborating with multidisciplinary teams to enhance AI fluency and drive scientific impact.

Highest-signal resume keywords

PhD In Machine Learning Or Related FieldStrong Python And PyTorchExperience Designing Rigorous ExperimentsExpertise In Multimodal Learning And Foundation ModelsExperience Applying AI In Life Sciences

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningArtificial IntelligenceStatistical AnalysisExperimental DesignModel DevelopmentOptimizationDebuggingEvaluation MethodologiesReproducible Research PracticeGenerative AI

Soft Skills

MentoringCollaborationCommunication

Tools & Technologies

JAXDistributed TrainingExperiment TrackingShared Evaluation Pipelines

Industry Keywords

Biomedical ResearchPharmaceuticalsHealthcareBiotechnologyClinical AI

Tech Stack

Tools & technologies

PythonPyTorch

About the role

Key responsibilities & impact

Develop, extend, and rigorously evaluate advanced AI methods that generate scientific insights, accelerate experimentation, and enable better decision-making across the drug discovery and development lifecycle
Own defined model components, benchmarks, evaluation frameworks, and research workflows influencing AI application across Pfizer's R&D organization
Develop and advance machine learning methods that learn from and reason across complex biomedical, clinical, molecular, and scientific data
Create, adapt, and improve foundation models, multimodal and graph-based approaches, generative methods, and agentic reasoning systems
Design clear hypotheses and controlled, reproducible experiments; compare approaches and identify success, failure, and rework areas
Design datasets, benchmarks, and validation strategies measuring robustness, calibration, temporal and cohort generalization, cross-therapeutic transfer, reasoning ability, interpretability, and failure modes
Work with AI engineers, computational biologists, and domain experts to test hypotheses and interpret results, uncertainty, and limitations transparently
Translate promising research into trusted capabilities deployed across multiple programs
Develop reusable methods, benchmarks, model components, and workflows on common infrastructure
Document, validate, and package frameworks for application to new disease areas
Contribute to scientific reviews and research planning
Mentor colleagues and help raise AI fluency across R&D
Collaborate with AI researchers, computational biologists, engineers, and clinical and domain experts to advance AI methods into drug discovery programs

Requirements

What you’ll need

PhD and 2+ years experience in Machine Learning, Artificial Intelligence, Computer Science, Statistics, Applied Mathematics, Computational Biology, or a related quantitative field preferred
Master's degree with 5+ years of relevant industry or research experience
Demonstrated experience developing, extending, or evaluating machine learning methodologies on hard, ambiguous, high value problems
Experience designing rigorous experiments and hypothesis-driven investigations
Experience building and comparing models using reproducible scientific approaches
Experience identifying limitations, biases, uncertainty, and failure modes
Experience translating research insights into meaningful outcomes and recommendations
Experience owning a research area, benchmark, model component, or scientific capability from concept through delivery
Strong Python and PyTorch, JAX or an equivalent framework
Knowledge of model development, optimization, debugging and evaluation methodologies
Experience with experimental design, statistical analysis and reproducible research practice
Familiarity with scalable ML workflows, including distributed training, experiment tracking, and shared evaluation pipelines
Depth in one or more of multimodal learning, foundation models, GNNs, generative AI, agentic systems, biological sequence or structure modeling, knowledge graphs, clinical AI, omics and biomedical data, or scientific ML
Publications, patents, open-source contributions, deployed research, or other evidence of scientific impact valued as indicators of expertise and execution
Experience applying AI in life sciences, biotechnology, healthcare, pharmaceuticals, or biomedical research is nice to have
Experience with biological, molecular, imaging, clinical, or real-world health data is nice to have
Experience building foundation models or multimodal learning systems for scientific applications is nice to have
Experience evaluating and implementing responsible AI methodologies is nice to have
Experience working across multidisciplinary scientific organizations is nice to have
Candidates must be authorized to be employed in the U.S. by any employer
This position requires permanent work authorization in the United States
U.S. work visa sponsorship is not available for this role now or in the future

Benefits

Comp & perks

Global Performance Plan bonus target of 17.5% of base salary
Share based long term incentive program
401(k) plan with Pfizer Matching Contributions
Additional Pfizer Retirement Savings Contribution
Paid vacation, holiday and personal days
Paid caregiver/parental and medical leave
Medical, prescription drug, dental and vision coverage
Hybrid work arrangement
Relocation assistance may be available based on business needs and/or eligibility