Manager, Data Science and AI

Pfizer; India; On-site; INR 3,000,000 - 5,400,000; Full time

Job summary

Pfizer is seeking a hands-on Manager, Data Science & AI to design, build, and deploy AI solutions that optimize marketing investments and channel decisions. You will lead end-to-end technical execution, from data ingestion and model selection to production deployment, with strong emphasis on data privacy, compliance, and scalable architecture.

You will partner with commercial AI leadership and business sponsors, owning architecture, integration with CRM, analytics dashboards, and regulatory

Qualifications

• Bachelor's degree in CS/Engineering/Math; Master's preferred; hands-on expertise in AI/ML systems.

Responsibilities

• Build and deploy production-grade AI agents for automating commercial workflows.
• Develop end-to-end RAG systems including document ingestion and embedding pipelines.
• Create and maintain data pipelines for model inference and agent consumption.
• Contribute to MLOps pipelines: versioning, deployment, evaluation, monitoring.

Skills

Python programming

ML systems

AI/ML concepts

Education

Bachelor's degree in CS/Engineering/Math

Master's degree preferred

Tools

LangChain

LlamaIndex

AutoGen

CrewAI

Job description

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

1. 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. 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. 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. 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. 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 acce