AI Data Science Lead (Secondment 12-18 Months)

Pfizer•Kalamazoo, MI

5d•Onsite

About The Position

This position reports to the Director, Site AI Transformation and develops the site's intelligence layer — predictive models, machine learning, optimization, forecasting, computer vision, and Large Language Models. Success is measured by business value from deployed models, not accuracy alone: a highly accurate model nobody uses has zero value. This role partners with the AI Data Engineers for data foundations and with the AI Data Security, Quality & Compliance Lead for validation, developing solutions such as batch-failure and downtime prediction, scheduling optimization, inventory recommendations, labor forecasting, and LLM operator assistants. As a member of the site AI team, this role owns the analytical design and modeling approach for prioritized AI and advanced analytics use cases. The role translates defined business problems into analytical framing, optimization approaches, experiments, models, and recommendations that can be tested, validated, and converted into measurable operational value.

Requirements

Must have a bachelor's degree with at least 5+ years of experience; OR a master's degree with at least 3+ years of experience; OR a PhD with 0+ years of experience; OR as associate's degree with 8+ years of experience; OR a high school diploma (or equivalent) and 10+ years of relevant experience.
Experience applying statistical analysis, machine learning, optimization, forecasting, advanced analytics, or AI methods to business problems.
Ability to translate analytical concepts into operational recommendations and communicate effectively with non-technical stakeholders.
Experience with data preparation, model validation, documentation, and structured problem solving.
Normal office role with frequent computer-based work, meetings, data review, business process analysis, and collaboration with site business areas.
Ability to perform complex problem solving, prioritization, risk assessment, quantitative analysis, and written documentation.
Periodic presence in manufacturing, laboratory, warehouse, or operational areas may be required; must follow applicable site safety, gowning, data integrity, and GMP expectations.
May require support outside normal business hours for critical site priorities, project milestones, production or compliance impacts, or executive reviews.
Occasional travel may be required for site, network, vendor, or enterprise alignment meetings.
This position requires permanent work authorization in the United States.

Nice To Haves

Experience with Python, SQL, R, Snowflake, Power BI/Tableau, ML Ops, model deployment, computer vision, NLP, or Generative AI preferred.
Experience in regulated manufacturing, pharmaceutical operations, supply chain, quality, maintenance, or laboratory environments preferred.
Knowledge of responsible AI, model risk management, explainability, validation, data integrity, and governance expectations preferred.

Responsibilities

Own analytical framing for assigned AI and advanced analytics use cases, including problem definition, hypotheses, model objectives, assumptions, data requirements, success measures, and decision outputs.
Develop predictive and machine learning models for use cases such as batch-failure prediction, downtime prediction, scheduling optimization, inventory recommendations, labor forecasting, and other operational decision-support needs.
Apply optimization algorithms, statistical analysis, forecasting, simulation, experimentation, and computer vision methods where they are the right fit for the business problem.
Design and run analytical experiments, model evaluations and scenario tests to determine what works, where it works, and what tradeoffs should be considered.
Generate clear recommendations from model outputs, including expected impact, confidence level, operational implications, limitations, and next-best actions for business owners.
Develop Generative AI / LLM solutions where appropriate while ensuring use cases have clear value, fit-for-use data, and appropriate validation expectations.
Build AI prototypes and MVPs with business stakeholders once the opportunity has been prioritized, focusing on analytical feasibility, model performance, usability, and measurable value.
Partner with AI Data Engineers to define required data sets, features, pipelines, data quality needs, and production-ready data flows.
Partner with the AI Data Quality & Compliance Lead on model validation, responsible-AI requirements, documentation, monitoring, and lifecycle controls.
Transition successful analytical solutions to enterprise platforms and Product Owners for sustainment, including model documentation, decision logic, performance expectations, and support needs.