Senior 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 Senior Manager, Data Science and AI, where you will lead the design, development, and deployment of AI‑solutions that measurably improve how the business invests across channels.   As a Senior Manager for Data Science & AI within GCA, you are the technical cornerstone of Pfizer's AI transformation. You don't just govern or advise you to design, build, and prove. You own the end-to-end technical architecture of AI initiatives: from data ingestion and model selection through RAG pipelines, agent orchestration, and production deployment. You are equally credible in a whiteboard session and in a code review, and you set up the bar for engineering quality across the team.   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 a manager who delegates the hard parts — you are the person the team turns to when the architecture is unclear, the data is messy, or the model isn't behaving ROLE RESPONSIBILITIES   1. Technical Architecture & Design   Define and own the end-to-end AI architecture for commercial initiatives from raw data through model inference and application layer.   Design and implement Retrieval-Augmented Generation (RAG) systems, including chunking strategies, embedding pipelines, vector store selection and retrieval optimization.   Architect multi-agent and agentic orchestration systems using frameworks such as LangChain, LlamaIndex, AutoGen, or CrewAI; define agent roles, tool use, memory, and human-in-the-loop patterns.   Select and configure Large Language Models (LLMs) including fine-tuning, prompt engineering, and context management for commercial use cases such as content generation, summarization, and intelligent search.   Design scalable data architectures that support AI workloads: data lakes, feature stores, vector databases, structured and unstructured data pipelines.   Evaluate and recommend LLM deployment strategies: cloud-hosted APIs (OpenAI, Azure OpenAI, AWS Bedrock, GCP Vertex AI), self-hosted models, and hybrid approaches.   2. Hands-On Development & Delivery   Write, review, and own production-quality code across the AI stack — Python, SQL, orchestration frameworks, and infrastructure-as-code.   Build and maintain MLOps pipelines: model training, evaluation, versioning, CI/CD for AI, and monitoring in production (drift detection, hallucination guardrails, latency tracking).   Develop and integrate APIs and automation workflows that connect AI capabilities to commercial business tools (CRM, content platforms, regulatory review systems).   Conduct and lead technical design reviews, architecture decision records (ADRs), and code reviews to ensure quality, security, and maintainability.   Prototype rapidly and iterate build proof-of-concepts that stress-test assumptions before committing to full-scale implementation.     3. Data Architecture & Engineering   Own the datastrategy for AI initiatives: schema design, data quality, lineage, governance, and access controls.   Build and optimize data pipelines that ingest, transform, and serve both structured and unstructured data for model training and inference.   Apply expertise in embedding models and semantic search to create knowledge bases that power RAG and intelligent retrieval systems.     4. Technical Leadership & Cross-Functional Partnership   Set the technical direction for AI initiatives; define standards,