Pfizer
Pharmaceutical
Manager,DataSpecialist
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“Manager, Data Specialist at Pfizer. Skills: Data engineering, Data product management, AI/ML enablement. Engage with commercial business stakeholders. Elicit, clarify, and document data and analytics requirements”
Industry & Context.
Permanent work authorization in the United States
What They're Looking For.
Must Have
Bachelor's degree with at least 4 years of experience, Master's degree with at least 2 years of experience, PhD with 0+ years of experience, Associate's degree with 8 years of experience, High school diploma and 10 years of relevant experience, Experience in a data-focused role, Translating business requirements into technical specifications, Experience in the pharmaceutical, biotech, or life sciences industry, Familiarity with commercial pharma data sources, Working knowledge of relational data concepts, Working knowledge of SQL, Working knowledge of data warehousing/lakehouse architectures, Familiarity with data pipeline development concepts, Familiarity with data modeling, Familiarity with data quality frameworks, Experience working within an Agile/Scrum delivery, Written and verbal communication skills
Nice to Have
Bachelor's degree in a quantitative, analytical, or business discipline
What You'll Do.
Engage with commercial business stakeholders
and document data and analytics requirements
Decompose business needs into data engineering work items
Define data product specifications
Define pipeline specifications
Define transformation logic
Define acceptance criteria
Facilitate working sessions between business users and engineering
and technical feasibility
Serve as primary point of contact for data-related
Ensure efficient backlog management
Author detailed data product specifications
Document business rules
Document data dictionaries
Document field-level definitions
Define and document data quality expectations
Define and document validation rules
Define and document SLA requirements
Maintain and improve data documentation artifacts
Support self-service discovery
Partner with data architects
Ensure data products align with enterprise semantic layer
Optimize data products for downstream consumption
Translate prioritized business requirements into user stories
Translate prioritized business requirements into epics
Translate prioritized business requirements into technical tasks
Provide domain context
Track progress of data engineering deliverables
Communicate status and impacts to business stakeholders
Validate delivered data products against requirements
Coordinate user acceptance testing
Champion data quality
Define data quality metrics
Monitor data quality metrics
Communicate data quality metrics
Ensure data products adhere to privacy standards
Ensure data products adhere to regulatory standards
Ensure data products adhere to data stewardship standards
Identify data lineage
Document data lineage
Identify data ownership
Document data ownership
Identify data usage policies
Document data usage policies
Understand data requirements of AI/ML use cases
Support data requirements for advanced analytics use cases
Support feature engineering inputs
Support model training datasets
Support inferencing pipelines
Coordinate with data scientists and ML engineers
Ensure data products are structured
Optimize data products for model development and deployment
Contribute to continuous improvement of data standards
Ensure commercial AI initiatives are built on a
How You'll Work.
Team & Collaboration
Business stakeholders; Data engineering team; Commercial analytics teams; AI/ML teams; Data architects; Data governance teams; Compliance teams; Data scientists; ML engineers; Business users; Engineering teams
Communication Scope
Written communication; Verbal communication; Non-technical communication
Process & Methodology
Agile, Scrum, Backlog management
Full Job Description
**ROLE SUMMARY** The AI Acceleration (AIA) function within the Chief Marketing Office (CMO) is the single, business-led engine that owns the design, delivery, and scale-up of priority AI capabilities across Commercial operations. AIA works in tight collaboration with various Pfizer functions to deploy and maintain production-grade AI solutions that simplify how we work and drive measurable value across all processes. The **Manager, Data Specialist** will serve as a critical bridge between business stakeholders and the data engineering team. This role will be responsible for deeply understanding commercial business processes, translating complex analytical and AI/ML requirements into actionable data engineering specifications, and ensuring the delivery of high-quality, governed data products that power commercial insights and AI-driven decision-making. The **Manager, Data Specialist** will partner closely with business translators, commercial analytics leads, data scientists, and AI/ML engineers to define data needs, validate data availability, and ensure that the data engineering team is building solutions that are fit for purpose, well-documented, and aligned to enterprise data governance standards. **ROLE RESPONSIBILITIES** **Requirements Translation & Stakeholder Partnership** * Engage directly with commercial business stakeholders—including Brand, Sales Operations, Market Access, Content Generation, and Medical —to elicit, clarify, and document data and analytics requirements. * Decompose high-level business needs into structured data engineering work items including data product definitions, pipeline specifications, transformation logic, and acceptance criteria. * Facilitate working sessions between business users and engineering teams to align on scope, timelines, and technical feasibility. * Serve as the primary point of contact for data-related inquiries from commercial analytics and AI/ML teams, triaging requests and ensuring efficient backlog management. *
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