Manager, Data Science and AI

ROLE SUMMARY

The Global Commercial Analytics (GCA) team within the organization is dedicated to transforming data into actionable intelligence enabling the business to remain competitive and innovative in a data-driven world.

Are you passionate about using data science AI and autonomous agents to unlock the return on every marketing dollar Do you thrive where advanced analytics agentic AI and commercial strategy meet Join our team as a Manager Data Science and AI where you will design build and deploy AIsolutions that measurably improve how the business invests across channels.

As a Manager Data Science & AI within GCA you are a hands-on practitioner and individual contributor at the technical core of Pfizers commercial AI transformation. This is not a people-management or oversight role it is a builder role. You own the end-to-end technical execution of AI initiatives: from data ingestion and model selection through RAG pipelines agent orchestration and production deployment. You are equally credible at the whiteboard and in a code review and you hold yourself to a high bar for engineering quality in everything you ship.

You partner directly with the International Commercial AI leadership program managers and business sponsors to translate ambitious commercial goals into sound scalable and compliant technical solutions. You are not someone who delegates the hard parts you are the person others rely on when the architecture needs defining the data is messy or the model isnt performing. You build the thing and you make it work.

ROLE RESPONSIBILITIES

1. Agentic AI Development & Deployment

• Build and deploy production-grade AI agents that automate commercial workflows optimize channel investment decisions and enable intelligent user interactions.

• Implement multi-agent orchestration systems using frameworks such as LangChain LlamaIndex AutoGen or CrewAI wiring agent roles tool use memory patterns and human-in-the-loop controls.

• Develop and maintain agentic pipelines that integrate with commercial business systems: CRM platforms marketing automation tools analytics dashboards and regulatory review workflows.

• Test and iterate on agent behavior evaluating accuracy reliability latency and hallucination risk before and after deployment.

• Tune agent performance through prompt engineering tool design and retrieval optimization based on real feedback from the business.

2. RAG Architecture & Generative AI Engineering

• Build RAG systems end-to-end: document ingestion chunking strategies embedding pipelines vector store integration and retrieval optimization.

• Implement and configure LLMs including prompt engineering context management and output guardrails for commercial use cases such as content generation market intelligence summarization and intelligent search.

• Work across cloud-hosted LLM APIs (Azure OpenAI AWS Bedrock GCP Vertex AI) and evaluate open-source model options where appropriate.

• Build and maintain knowledge bases that power AI applications keeping underlying data accurate current and well-structured.

3. Data Engineering & Pipelines

• Build and maintain data pipelines that ingest transform and serve structured and unstructured commercial data for model inference and agent consumption.

• Apply working expertise in embedding models and vector databases (Pinecone Weaviate Azure AI Search pgvector) to enable semantic search and retrieval.

• Ensure pipelines meet data privacy and compliance requirements applying pseudonymization lineage tracking and access controls appropriate to the data classification.

• Collaborate with data and analytics teams to align on schemas data quality standards and the data foundations that AI systems depend on.

4. MLOps & Code Quality

• Contribute to MLOps pipelines: model versioning deployment automated evaluation and production monitoring including drift detection and latency tracking.

• Write clean tested and maintainable Python code; contribute to shared libraries internal tooling and reusable components.

• Build APIs and integrations that surface AI capabilities to commercial business tools and non-technical end users.

• Document what you build architecture notes system designs and runbooks so the work is understandable and maintainable.

5. Technical Collaboration & Delivery

Work closely with program managers and commercial analytics stakeholders to scope technically grounded solutions aligned to business needs. Participate in design and code reviews. Engage with compliance legal and privacy stakeholders to ensure AI outputs are explainable and appropriate. Research new frameworks and tools bringing forward evidence-backed recommendations when better options are available.

BASIC QUALIFICATIONS

Education: Bachelors degree in Computer Science Engineering Mathematics Statistics or a related technical field. Masters degree preferred; equivalent demonstrated hands-on expertise in AI/ML systems accepted in lieu of formal degree.

Experience:

• 6 years of progressive hands-on experience in software engineering data science AI/ML or data engineering with consistent evidence of building and shipping production systems not only prototypes.

• Working practitioner-level expertise in Generative AI: LLM integration prompt engineering context window management and output validation/guardrails.

• Hands-on experience building and deploying RAG architectures including embedding model selection chunking strategies hybrid search and retrieval quality evaluation.

• Practical experience with agentic AI frameworks (LangChain LlamaIndex AutoGen CrewAI or equivalent): implementing agents tool use and memory in real applications.

• Familiarity with vector databases (Pinecone Weaviate Azure AI Search pgvector Chroma or equivalent) and semantic search.

• Solid Python skills; proficiency in SQL; experience with at least one cloud AI platform (Azure OpenAI / Azure ML AWS Bedrock / SageMaker or GCP Vertex AI).

• Hands-on experience building data pipelines for AI workloads ingestion transformation embedding and serving of structured and unstructured data.

• Exposure to MLOps practices: deployment pipelines model monitoring and evaluation in production environments.

PREFERRED QUALIFICATIONS

• Masters degree in Computer Science Data Science AI or a related quantitative field.

• Experience in commercial pharma healthcare technology or a regulated industry with familiarity with promotional-review workflows MLR processes or GxP/HIPAA/data-privacy compliance.

• Hands-on experience with commercial analytics use cases: marketing mix modelling channel attribution next-best-action systems or AI-powered customer segmentation.

• Experience adapting or fine-tuning open-source foundation models (Llama Mistral Falcon or equivalent) for domain-specific applications.

• Familiarity with responsible AI frameworks bias evaluation or AI governance tooling (e.g. Azure AI Content Safety Guardrails AI Giskard).

• Relevant certifications: AWS Certified Machine Learning Specialty Azure AI Engineer Associate GCP Professional ML Engineer or equivalent.

Pfizer is an equal opportunity employer and complies with all applicable equal employment opportunity legislation in each jurisdiction in which it operates.

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