Manager, Market Access Data & Delivery Lead

Indialead

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Quick Summary

Requirements Summary

experience building data products on Databricks, including curated (gold) layers, and taking them from concept through release and scale.

Technical Tools

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This role is the technical and delivery counterpart to the US AI project lead. You are the Hyderabad lead for the team's AI work: building the data products that the AI capability runs on, shaping the semantic and context layer with CBIT, and keeping data quality and evaluation under control. You are also the Hyderabad point of contact across the AI initiatives, working with Data Strategy, CBIT and vendors. The role needs hybrid depth: strong data and AI skills, plus enough market access knowledge to build the right context.

Responsibilities

~1 min read

Data products and data foundation

→Build data products for Market Access, moving data from CSCAN into Databricks.
→Transition the current BoW into the gold layer in Databricks.
→Handle ingestion into the database and create the data models the AI capability depends on, working to the data design the team provides.

Semantic and context layer

→Build the semantic and context layer with CBIT, using the market access context and metric definitions gathered by the US lead and the brand-focused team members.
→Keep data definitions consistent across brands, personas and AI initiatives.

Quality, evaluation and support

→Run evaluation, data quality checks and monitoring for the data products and the AI outputs built on them.
→Provide documentation and ongoing support for the data products.

Coordination

→Act as the single point of contact in Hyderabad across the AI initiatives, aligning timelines and data products.
→Lead from Hyderabad for Data Strategy, CBIT and vendors.
→Work closely with the Market Access Analytics lead and the India team lead, and with the US AI project lead on priorities and releases.

Requirements

~1 min read

Data and product development: experience building data products on Databricks, including curated (gold) layers, and taking them from concept through release and scale. [Years of experience to be confirmed]
AI and agentic architecture: working knowledge of agent frameworks, semantic layers, guardrails and evaluation.
Pharma data sets: understanding of pharmacy and medical benefits data and their limits.
Market access knowledge: enough payer, access and coverage context to build and check the semantic layer; deeper knowledge is a strong plus.
Data quality and monitoring: experience setting up evaluation, quality checks and monitoring.
Project management: coordinating timelines and dependencies across teams and vendors.
Documentation and communication: clear written documentation, and comfort working with business, IT and vendor teams.

Nice to Have

~1 min read

Experience with syndicated and gross-to-net data.
Exposure to contracting and 340B analytics, which are expected to become workstreams next year.
Familiarity with enterprise AI assistants such as Claude.
Experience working with onshore teams across US and India time zones.