Senior Decision Scientist, Maven Clinic

• 5+ years of relevant analytics or data science experience, with a track record of cross-functional or cross-surface work
• Fluency in SQL, DBT, and 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 both internal and 3rd party sources
• An ability to juggle long-term, strategic projects alongside ad hoc, urgent requests
• Excellent collaboration and communication skills, with a proven ability to turn data into direction for cross-functional stakeholders

Desirable

• This isn't a vertical-specific 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
• You're equally at home in the details of measurement and in the big-picture framing that turns data into direction
• You know the highest-leverage work often isn't answering one question well—it's building the systems and shared language that let everyone answer their own questions better
• Own our key cross-functional reports end to end—understanding not just how they're built but why each metric matters and how the teams that depend on them make decisions
• Consolidate fragmented reporting into a coherent, shared view across clinical product, member experience, and our AI and healthcare work, so every team operates from the same source of truth
• Surface the insights that only become visible when you look across surfaces rather than within a single one, and translate them into clear direction for stakeholders
• Partner on the design of scalable data architecture (primarily using SQL and Python), reducing one-off requests and letting every team answer its own questions faster
• Define success metrics with partners across Product, Engineering, and Operations, and pair rigorous measurement with big-picture framing
• Establish the shared language, standards, and best practices that make the whole team faster and sharper—mentoring other analysts along the way