Posted 2mo ago

Manager / Sr. Manager - Commercial Data Product (Pharma/ Life sciences)

@ Tiger Analytics
Boston, Massachusetts, United States
OnsiteFull Time
Responsibilities:partner stakeholders, own backlog, design models
Requirements Summary:10+ years in commercial analytics/data product management in pharma/life sciences; launches, segmentation, KPIs; Snowflake data modeling; Power BI semantic model; backlog ownership; independent in greenfield builds.
Technical Tools Mentioned:Snowflake, Power BI
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Job Description

Tiger Analytics is an advanced analytics consulting firm. We are the trusted analytics partner for several Fortune 1000 companies, enabling them to generate business value from data. Our consultants bring deep expertise in Data Science, Machine Learning and AI. Our business value and leadership has been recognized by various market research firms, including Forrester and Gartner.

We are seeking a Manager/Senior Manager with a strong Life Sciences background and hands-on commercial analytics experience to launch a Kidney Commercial Data Product to support an upcoming pharma launch. This role will lead a small, dedicated pod to design and build a Snowflake-native, Power BI–aligned data product from the ground up. The position gives an opportunity to focus on data product ownership, metric design, and stakeholder alignment.

Key Responsibilities

  • Partner with Life Sciences stakeholders to understand business requirements and translate them into analytical solutions
  • Own the end-to-end commercial data product for a kidney launch, from vision to execution.
  • Translate launch strategy into segmentation frameworks, KPIs, and data products used by commercial leadership.
  • Drive stakeholder alignment across Marketing, Sales, Market Access, and Analytics
  • Own and prioritize the product backlog, ensuring alignment with launch milestones. Partner with the engineering team to design Snowflake-native data models optimized for scalability and reuse.
  • Ensure strong Power BI semantic model understanding (measures, relationships, performance).
  • Enable self-service analytics through well-designed datasets—not dashboards alone.
  • Establish KPI governance, adoption tracking, and success metrics for the product.
  • Lead a small cross-functional pod (data engineers, analysts).
  • Operate in an agile delivery model with clear sprint outcomes and executive visibility.
  • Act as the primary interface between business stakeholders and the technical team.