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 AI‑solutions 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 Pfizer's 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 isn't performing. You build the thing, and you make it work.

Role Responsibilities

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.
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.
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.
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.
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: Bachelor's degree in Computer Science, Engineering, Mathematics, Statistics, or a related technical field. Master's 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

Master's 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.

Work Location Assignment: Hybrid

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 on Pfizer Careers.

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