Sr. Applied Behavioral Scientist

We are seeking a Senior Applied Behavioral Scientist to join our growing US-based Behavioral Science team as the senior technical lead for key components of our system.

This role combines two functions that are closely intertwined in our product: owning the decision logic that determines what the system does for a user, and owning the statistical and causal-inference architecture that determines whether an intervention is working for that user.

On the decision logic side, you will lead development of our just-in-time adaptive intervention design system in partnership with the AI Team — a core system that determines when and how the app engages users, guiding them at moments that matter most.

On the causal inference side, you will own the personalized learning loop that determines intervention effectiveness at the individual level — specifying the attribution scheme, defining instrumentation requirements for Engineering, and partnering with ML on the recommendation and causal-inference architecture.

You will also have the opportunity to publish.

This is a high-leverage, intellectually demanding role for a senior behavioral scientist who combines data science-level rigor with behavior change theory and hands-on expertise in building decisioning systems, collecting and working with behavioral data, causal inference and adaptive experimentation, and who is comfortable translating both into practical, sprint-compatible infrastructure at an industry pace.

• Support Behavioral Taxonomy Development: Design and build a structured behavioral taxonomy in close collaboration with the AI Team. Define the taxonomy of behavioral targets, barrier profiles, BCT mappings, and intervention modalities. Ensure the taxonomy is structured for machine readability and downstream use in the recommendation system.
• Translate Academic Frameworks into Applied Schema: Convert the BCTTv1, COM-B framework, and relevant behavior change evidence into a practical classification system that Engineering and ML teams can operationalize.
• Review and Validate Behavioral Logic and Algorithms: Provide expert review of intervention logic, BCT-to-barrier mappings, and taxonomy edge cases. Flag areas where the system’s behavioral logic deviates from the evidence base.
• Maintain Evidence Standards: Bring a publication-grade standard for evidence evaluation. Help the team distinguish between well-supported, plausible, and speculative behavioral claims within the taxonomy.