Senior Decision Scientist, Measurement & Insights

About the Role

Maven is looking for an experienced decision scientist who works across the seams—connecting clinical product, member experience, and our AI and healthcare initiatives into one coherent story.

You'll tie the narrative together: building cross-functional reporting that gives every team a shared view of what's working and why, and partnering on the data architecture that makes the whole team faster. Instead of owning one surface, you'll work the connective tissue between them—surfacing insights that only appear when you look across the entire experience.

Responsibilities

• Own key cross-functional reports end to end—understanding how they're built, why each metric matters, and how dependent teams make decisions.
• Consolidate fragmented reporting into a coherent, shared view across clinical product, member experience, and AI/healthcare initiatives.
• Surface cross-surface insights and translate them into clear direction for stakeholders.
• Partner on designing scalable data architecture (primarily using SQL and Python) to enable self-service analytics.
• Define success metrics with Product, Engineering, and Operations partners, combining rigorous measurement with strategic framing.
• Establish shared language, standards, and best practices across the team while mentoring other analysts.

Qualifications

Minimum Qualifications

• 5+ years of relevant analytics or data science experience with a track record of cross-functional work.
• Fluency in SQL and dbt, alongside a strong background in data visualization.
• Proficiency with at least one BI tool (Tableau, Looker).
• Hands-on experience with Python or R.
• Demonstrated ability to design scalable data structures and reporting from internal and 3rd party sources.
• Ability to manage long-term strategic projects alongside urgent ad hoc requests.
• Strong collaboration and communication skills.

Preferred Qualifications

• Experience working with event-based data.
• Knowledge of experimentation and/or statistical modeling.
• Experience building shared measurement frameworks or semantic/metrics layers across multiple teams.