Data Science Manager

ROLE SUMMARY

Applied Intelligence is a high-velocity team purpose-built to do one thing exceptionally well: take the hardest, most ambiguous AI/ML problems in the enterprise and rapidly determine whether they are solvable, how they should be solved, and what it will take to make them real at scale. Our work de-risks high-impact AI/ML investments through fast, disciplined experimentation, and directly shapes what gets scaled, what gets stopped, and where the organization invests next.

As Manager you will build and lead a small team of exceptional applied AI engineers and data scientists while staying deeply hands-on yourself, contributing directly to architecture, code, and experimental design. This is not a sandbox: every proof of concept your team builds is developed with the engineering hygiene of production code, because the best prototypes become the foundation of enterprise systems.

The role rests on three pillars, and we expect strength in all three: business judgment to choose and frame the right problems, technical depth to build and validate AI/ML solutions, and engineering discipline to make the work reproducible, reviewable, and ready to scale.

Day to day, you will work in 2 to 6 week prototype cycles, translating ambiguous commercial problems into testable hypotheses, building models and data pipelines, and delivering evaluation evidence that supports clear go/no-go decisions.

ROLE RESPONSIBILITIES

Business Skills and Problem Framing

• Partner with commercial, product, and functional leaders to identify where AI/ML can create real value, and to say clearly when it cannot.

• Translate ambiguous business problems into scoped, testable hypotheses with defined success criteria before any code is written.

• Deliver clear, outcome-oriented recommendations on what to scale, what to stop, and where to invest next, with the evidence and the limitations stated plainly.

• Communicate results to technical and non-technical audiences alike, including senior stakeholders, and defend methodological choices under scrutiny.

• Manage a portfolio of concurrent prototypes, balancing speed against rigor and making explicit trade-off calls on scope and effort.

Building AI/ML Models

• Contribute hands-on to model development: feature engineering, model selection, training, tuning, and evaluation across classical ML, deep learning, and LLM-based approaches.

• Own experimental design with statistical and methodological rigor, including validation strategy, baselines, evaluation metrics, error analysis, and avoidance of leakage and other common pitfalls.

• Build data pipelines and integrations that make prototypes real, working with enterprise, third-party, and unstructured data sources.

• Apply GenAI and LLM techniques where they fit the problem, including retrieval-augmented generation, agentic workflows, prompt engineering, and systematic evaluation of model outputs.

Engineering Discipline

• Enforce GitHub-based workflows as a baseline: branching strategy, pull requests, code review, and traceable history on every project.

• Build and maintain CI/CD pipelines for models and data products, including automated testing, linting, and reproducible builds.

• Deliver reproducible, production-ready code: containerization, dependency and environment management, configuration over hardcoding, and clear documentation.

• Apply MLOps practices to produce deployment-ready artifacts, including experiment tracking, model versioning, monitoring, and rollback strategies appropriate for regulated environments.

• Use AI coding tools (for example Claude Code, Cursor, GitHub Copilot) to accelerate delivery and establish team standards for how AI-generated code is reviewed, tested, and held to the same quality bar as any other code.

• Prepare validated prototypes for structured handoff to industrialization, production, or IT teams, including runnable artifacts, runbooks, and acceptance criteria, and support the transition from build to operations.

• Drive modular, reusable code and the extraction of common patterns to reduce rework across projects.

BASIC QUALIFICATIONS

• BA/BS with 5+ years of experience in AI/ML, data science, or applied research, with substantial hands-on technical delivery.

• Demonstrated ability to translate ambiguous business problems into analytical approaches and to communicate results and recommendations to business audiences.

• Strong hands-on coding ability in Python and active proficiency with modern ML frameworks (PyTorch, TensorFlow, or scikit-learn), with regular coding in production or prototype contexts.

• Solid grasp of ML/AI fundamentals and experimental design, including validation strategy, evaluation metrics, and avoidance of common pitfalls.

• Practical experience with software engineering practice: Git/GitHub workflows, code review, CI/CD tooling (GitHub Actions, Jenkins, or similar), containerization (Docker), and reproducibility tooling.

• Working experience with AI-assisted coding tools and a considered point of view on where they help and where they need guardrails.

• Experience mentoring or leading engineers or data scientists, formally or informally.

• Collaborative and results-oriented, with the ability to manage multiple priorities in fast, resource-constrained environments.

• Fluent in English, both written and verbal.

PREFERRED QUALIFICATIONS

• Advanced degree (MS or PhD) in Computer Science, Statistics, Computational Biology, Engineering, or a related quantitative field.

• Direct people-management experience, including hiring and performance development.

• Experience applying AI/ML to commercial functions in pharmaceutical or life sciences settings, such as forecasting, segmentation, promotional effectiveness, or real-world evidence analytics.

• Knowledge of the pharma healthcare data landscape (for example IQVIA, Rx/sales, claims data).

• Experience working in regulated industries such as pharmaceutical, biotech, medical devices, or financial services.

• Hands-on experience building and evaluating LLM or agentic applications beyond prototypes.

• Cloud platform experience (AWS, Azure, GCP).

• ML platform experience (Snowflake ML, SageMaker, Databricks ML, Vertex AI, or similar).

• Familiarity with model deployment, monitoring, and container orchestration patterns suitable for enterprise, regulated environments.

• Experience creating reusable engineering patterns, templates, or runbooks for broader team or organizational use.

EEO (Equal Employment Opportunity) & Employment Eligibility 

Pfizer is committed to equal opportunity in the terms and conditions of employment for all employees and job applicants without regard to race, color, religion, sex, sexual orientation, age, gender identity or gender expression, national origin, or disability.

To learn more about acceptable and prohibited uses of AI during the recruitment process, please review our candidate AI-use guidelines available on Pfizer Careers.