Manager, Data Science and AI

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.

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