Manager, Market Access Data & Delivery Lead
KMK Consulting, Inc. (company site, opens in a new tab)On-siteDirect career pageEst. 12–22 LPA
Posted 17h ago
Reach people at KMK Consulting, Inc.
People at this company you can reach
Log in to see who works here
Create a free account or log in to see the people at KMK Consulting, Inc. you could write to:
- Recruiters & HR
- People in this function
- Work addresses
Your match
Against your primary resume
Log in to see your match score
Create a free account or log in to unlock your CV match score across:
- Role fit
- Experience
- Industry relevance
Protocol Intelligence
How competitive this role is
Competition
Background fit
Strictness
Log in to see why each signal reads the way it does.
Job description
Structured overview of role & requirements
About This Role
- Lead development of Market Access data products on Databricks, including transitioning data into a curated gold layer and managing ingestion and data modeling.
- Build and maintain the semantic and context layer for AI capabilities with CBIT, ensuring consistency of data definitions across brands, personas, and initiatives.
- Act as Hyderabad AI initiatives point of contact; coordinate across Data Strategy, CBIT, vendors, and collaborate with US and India leads on priorities, timelines, and deliverables.
Minimum Requirements
- Experience building and scaling data products on Databricks, including gold layer development (years of experience not explicitly mentioned).
- Working knowledge of AI architectures involving agent frameworks, semantic layers, guardrails, and evaluation processes.
- Understanding of pharma pharmacy and medical benefits data sets and their limitations.
- Familiarity with market access concepts sufficient to develop and validate semantic layers; deeper knowledge preferred but not mandatory.
Ideal Candidate Profile
- Strong hybrid expertise combining data product development with domain knowledge in market access and pharma data.
- Experienced in managing cross-team coordination and project management across multiple stakeholders and time zones (US and India).
- Comfortable working with AI evaluation, data quality monitoring, and vendor collaboration in a complex technical environment.