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The Role
Hands-on individual contributor who designs, builds, and deploys production-grade generative AI, RAG, and agentic systems for commercial analytics. Responsibilities include end-to-end RAG and agent pipelines, vector DB and embedding engineering, cloud LLM integrations, data pipelines, MLOps, model monitoring, prompt engineering, and cross-functional collaboration with compliance and business stakeholders.
Summary Generated by Built In
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. Agentic AI Development & Deployment
2. RAG Architecture & Generative AI Engineering
3. Data Engineering & Pipelines
4. MLOps & Code Quality
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: 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:
PREFERRED QUALIFICATIONS
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 .
Marketing and Market Research
#BI-Hybrid
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. 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: 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 .
Marketing and Market Research
#BI-Hybrid
Skills Required
- Bachelor's degree in Computer Science, Engineering, Mathematics, Statistics, or related technical field (or equivalent hands-on expertise).
- 6+ years progressive hands-on experience in software engineering, data science, AI/ML, or data engineering with production systems experience.
- Practitioner-level expertise in Generative AI including LLM integration, prompt engineering, context window management, and output guardrails.
- Hands-on experience building and deploying RAG architectures: document ingestion, chunking, embeddings, vector stores, and retrieval optimization.
- Practical experience with agentic AI frameworks (e.g., LangChain, LlamaIndex, AutoGen, CrewAI) implementing agents, tools, and memory.
- Familiarity with vector databases (Pinecone, Weaviate, Azure AI Search, pgvector, Chroma) and semantic search.
- Solid Python development skills and ability to write clean, tested, maintainable code.
- 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 structured and unstructured data.
- Exposure to MLOps practices: model versioning, deployment pipelines, production monitoring, drift detection, and evaluation.
- Experience building APIs and integrations to surface AI capabilities to business tools and non-technical users.
- Master's degree in Computer Science, Data Science, AI, or related quantitative field.
- Experience in commercial pharma, healthcare technology, or regulated industries (promotional-review workflows, MLR, GxP/HIPAA familiarity).
- Hands-on experience with commercial analytics use cases: marketing mix modelling, channel attribution, next-best-action, or customer segmentation.
- Experience adapting or fine-tuning open-source foundation models (Llama, Mistral, Falcon, or equivalent).
- Familiarity with responsible AI frameworks and governance tooling (Azure AI Content Safety, Guardrails AI, Giskard).
- Relevant certifications (e.g., AWS Certified Machine Learning Specialty, Azure AI Engineer Associate, GCP Professional ML Engineer).
What the Team is Saying
Daniel
Diagnostics Launch Lead
Anna
R&D Privacy Steward
Esteban
Specialist
Pfizer
Pfizer Compensation & Benefits Highlights
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Healthcare Strength — Official materials describe comprehensive medical, dental, vision, and mental-health support, plus fertility/family‑building and transgender‑inclusive coverage; eligible Pfizer medications are noted as available at no cost in U.S. plans. Wellness resources such as telehealth and preventative programs are also emphasized.
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Retirement Support — Company documents highlight a 401(k) with matching contributions plus an additional Retirement Savings Contribution beyond the match. Financial planning support and company‑paid life and disability insurance further bolster long‑term security.
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Leave & Time Off Breadth — Corporate pages and job postings describe paid vacation and holidays, caregiver leave, and paid parental leave for both parents. Materials also note that details can vary by role and location.
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The Company
What We Do
Our purpose ensures that patients remain at the center of all we do. We live our purpose by sourcing the best science in the world; partnering with others in the healthcare system to improve access to our medicines; using digital technologies to enhance our drug discovery and development, as well as patient outcomes; and leading the conversation to advocate for pro-innovation/pro-patient policies.
Why Work With Us
We are the inventors, the problem solvers, the big thinkers — those who surmount any hurdle to deliver breakthrough medicines to the people who are counting on them the most.
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Pfizer Offices
Hybrid Workspace
Employees engage in a combination of remote and on-site work.
Typical time on-site:
2.5 days a week
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