Manager, Data Specialist
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.
Data Product Definition & Documentation
Author detailed data product specifications including source-to-target mappings, business rules, data dictionaries, and field-level definitions.
Define and document data quality expectations, validation rules, and SLA requirements in collaboration with data engineering and governance teams.
Maintain and continuously improve data documentation artifacts within the enterprise data catalog to support self-service discovery and AI-readiness.
Partner with data architects to ensure proposed data products align with the enterprise semantic layer and are optimized for downstream AI/ML and analytics consumption.
Data Engineering Backlog Management
Translate prioritized business requirements into well-defined user stories, epics, and technical tasks within the data engineering development backlog.
Collaborate with data engineers to refine tickets, clarify ambiguities, and provide domain context that enables efficient, high-quality delivery.
Track progress of data engineering deliverables, identify blockers, and communicate status and impacts to business stakeholders in a clear, non-technical manner.
Validate delivered data products against requirements and coordinate user acceptance testing with business teams prior to production release.
Data Quality & Governance
Champion data quality by defining, monitoring, and communicating metrics that measure the reliability, completeness, and timeliness of commercial data assets.
Work with data governance and compliance teams to ensure data products adhere to applicable privacy, regulatory, and data stewardship standards relevant to commercial pharma (e.g., IQVIA, CRM, HCP/HCO data).
Identify and document data lineage, ownership, and usage policies for commercial data domains.
AI & Analytics Enablement
Understand and support the data requirements of AI/ML and advanced analytics use cases, including feature engineering inputs, model training datasets, and inferencing pipelines.
Coordinate with data scientists and ML engineers to ensure data products are structured, enriched, and accessible in formats optimized for model development and deployment.
Contribute to the continuous improvement of data standards that ensure commercial AI initiatives are built on a trusted, governed data foundation.
Basic Qualifications
Applicant must have a bachelor's degree with at least 4 years of experience; OR a master's degree with at least 2 years of experience; OR a PhD with 0+ years of experience; OR as associate's degree with 8 years of experience; OR a high school diploma (or equivalent) and 10 years of relevant experience.
Experience in a data-focused role such as business analyst, data analyst, data product manager, or comparable function within a data-intensive organization.
Demonstrated experience translating business requirements into technical specifications for data engineering or BI/analytics development teams.
Experience in the pharmaceutical, biotech, or life sciences industry, particularly within a commercial analytics or sales operations function.
Familiarity with commercial pharma data sources such as IQVIA (APLD, NPA, NSP), Symphony Health, Veeva CRM, patient claims data, or HCP/HCO affiliation data.
Working knowledge of relational data concepts, SQL, and data warehousing/lakehouse architectures (e.g., Snowflake, Databricks, Redshift).
Familiarity with data pipeline development concepts (ETL/ELT), data modeling, and data quality frameworks.
Experience working within an Agile/Scrum delivery model; proficiency with tools such as Jira, Confluence, or equivalent.
Strong written and verbal communication skills with the ability to interface effectively with both technical and non-technical audiences.
Preferred Qualifications
Bachelor's degree in a quantitative, analytical, or business discipline (e.g., Data Science, Information Systems, Statistics, Business Analytics).
5+ years of experience in a data-focused role such as business analyst, data analyst, data product manager, or comparable function within a data-intensive organization.
Work Location Assignment: Hybrid
Relocation assistance may be available based on business needs and/or eligibility.
Candidates must be authorized to be employed in the U.S. by any employer.
U.S. work visa sponsorship (such as TN, O-1, H-1B, etc.) is not available for this role now or in the future.
Sunshine Act
Pfizer reports payments and other transfers of value to health care providers as required by federal and state transparency laws and implementing regulations. These laws and regulations require Pfizer to provide government agencies with information such as a health care provider’s name, address and the type of payments or other value received, generally for public disclosure. Subject to further legal review and statutory or regulatory clarification, which Pfizer intends to pursue, reimbursement of recruiting expenses for licensed physicians may constitute a reportable transfer of value under the federal transparency law commonly known as the Sunshine Act. Therefore, if you are a licensed physician who incurs recruiting expenses as a result of interviewing with Pfizer that we pay or reimburse, your name, address and the amount of payments made currently will be reported to the government. If you have questions regarding this matter, please do not hesitate to contact your Talent Acquisition representative.
EEO & Employment Eligibility
Pfizer is committed to equal opportunity in the terms and conditions of employment for all employees and job applicants without regard to race, color, religion, sex, sexual orientation, age, gender identity or gender expression, national origin, disability or veteran status. Pfizer also complies with all applicable national, state and local laws governing nondiscrimination in employment as well as work authorization and employment eligibility verification requirements of the Immigration and Nationality Act and IRCA. Pfizer is an E-Verify employer. This position requires permanent work authorization in the United States.
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