Manager, Access Data Products & AI Analytics (Bengaluru)

OBJECTIVES / PURPOSE We are seeking a technically strong and business-oriented data analytics manager to join Takeda's GCC Commercial Analytics & Insights organization in India as Manager, Access Data Products & AI Analytics. This role will support the delivery and operational excellence of Takeda's Patient and Market Access data product portfolio. The Manager will partner closely with the Senior Manager, Access Data Product Strategy & AI Enablement, the U.S. Access Data Products Director, Patient Access and Market Access (PAMA), DD&T; ICC teams to translate PAMA business needs into trusted, governed, analytics-ready data products and solutions. The ideal candidate will bring strong hands-on data analytics skills, commercial pharma data experience, and practical exposure to contemporary data platforms and AI-enabled analytics. Experience with Databricks, SQL, BI tools, AI/BI capabilities, conversational analytics, GenAI, LLMs, small language models, and natural language data exploration is preferred. Candidates with prior experience at organizations such as ZS, Axtria, IQVIA, or similar life sciences analytics and data consulting firms would be well aligned to this role. This role requires a hands-on manager who can manage product delivery workstreams, perform data analysis, validate business rules, support reporting and AI/BI enablement, and ensure data products and analytics are accurate, reusable, and fit for decision-making. ACCOUNTABILITIES - Access data product delivery and execution: Translate business questions and stakeholder needs into clear requirements, user stories, acceptance criteria, source-to-target mappings, data definitions, and validation scenarios. - Partner with the Senior Manager, U.S. Access Data Products Director, DD&T;, data engineering teams, analytics teams, and reporting teams to ensure assigned data products are delivered with quality and business relevance. - Maintain product backlogs, delivery trackers, issue logs, release notes, documentation, and validation evidence for assigned Access data products. - Support standardization and reuse across Access data products, helping reduce fragmented, manual, or one-off datasets. - Technical data analysis and Databricks enablement: Perform hands-on data analysis to profile data, validate logic, investigate discrepancies, and confirm data readiness for analytics and reporting use cases. - Use SQL and modern data platforms, especially Databricks or similar environments, to support data exploration, transformation validation, reconciliation, and analytics-ready dataset preparation. - Partner with data engineers and platform teams to review data models, transformation logic, refresh processes, and consumption layers. - Support creation and validation of semantic layers, curated data marts, reusable business logic, and certified metrics for Access analytics and reporting. - Identify opportunities to automate recurring data checks, validation routines, reporting support, and data quality monitoring. - AI/BI and conversational analytics support: Support AI/BI and GenAI-enabled analytics use cases that improve data discovery, self-service reporting, data quality investigation, documentation, and insight generation. - Assist in developing and validating conversational analytics capabilities, including natural language querying, governed semantic layers, reusable analytical prompts, and business-friendly data exploration experiences. - Bring practical understanding of LLMs, small language models, prompt engineering, retrieval-augmented generation, and responsible AI concepts as they apply to commercial pharma data and analytics. - Partner with the Senior Manager and platform teams to test AI-enabled workflows and ensure outputs are accurate, explainable, governed, and appropriate for business use. - Help business users adopt AI-enabled analytics capabilities by supporting training materials, FAQs, examples, documentation, and issue resolution. - Data quality, validation, and governance: Perform hands-on data quality checks, including completeness, timeliness, accuracy, consistency, business rule adherence, and reconciliation against source or control totals. - Create and maintain validation scripts, QA checklists, exception reports, control files, and issue-resolution documentation. - Investigate data discrepancies, identify root causes, coordinate remediation with technical teams, and communicate business impacts clearly. - Support data quality KPIs and monitoring routines for assigned Access data products. - Ensure business rules, metric definitions, lineage, source-to-target mappings, transformation logic, assumptions, limitations, and known caveats are documented and maintained. - Follow Takeda's data governance, privacy, metadata, lineage, and appropriate-use standards for healthcare and commercial pharma data. - Reporting and analytics enablement: Support trusted home office, market access, patient services .