Sr. Applied Behavioral Scientist
Vesync.com· H - Business
Tustin, CA$200kFull-timePosted 2mo ago
About the role
Senior Applied Behavioral Scientist to lead just-in-time adaptive intervention design and causal-inference learning loop within VeSync’s Behavioral Science team. Combines decisioning system ownership with causal inference architecture, collaborating with AI/ML to influence user interventions and publish findings.
Responsibilities
- 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.
- Lead JITAI System Development: Design and build the just-in-time adaptive intervention design system in close collaboration with the AI Team — the decision logic that determines whether, what, and when to prompt a user at each moment of opportunity in the app.
- Define Decision Points & Tailoring Variables: Specify the decision points at which the system evaluates whether to intervene, and the tailoring variables — behavioral state, context, receptivity, and prior response history — that inform each decision.
- Design Decision Rules from Behavioral Evidence: Translate BCTTv1, COM-B, and relevant behavior change evidence into decision rules that map tailoring-variable values to specific intervention options, ensuring every prompt delivered is behaviorally grounded.
- Own the Behavioral Taxonomy & Delivery Constraints: Define and maintain the taxonomy of prompts, nudges, and intervention modalities the decision logic can select from, along with rules for cadence, cooldowns, and sequencing that protect against message fatigue and habituation.
- Review, Validate & Maintain Evidence Standards: Provide expert review of decision rule performance in production, flag areas where real-world behavior deviates from the evidence base, and bring a publication-grade standard for distinguishing well-supported, plausible, and speculative behavioral claims driving the system.
- Own the Learning Loop Attribution Scheme: Design the statistical and causal framework for the Learning Loop before product launch. Specify micro-randomization strategies, off-policy evaluation methods, and individual-level effect estimation approaches that will power personalized intervention delivery.
- Define Instrumentation Requirements: Partner with Engineering to specify the event logging and data infrastructure needed to support the learning loop. Ensure instrumentation is in place before launch to enable attribution and effect estimation.
- Specify Adaptive Trial Designs: Design and advise on adaptive experimentation methodologies including Multi-Arm Bandits (MABs), Micro-Randomized Trials (MRTs), and contextual exploration strategies appropriate for within-person behavioral data.
- Support N-of-1 Experiment Infrastructure: Develop the analytical framework for N-of-1 self-experiments within the product. Define how individual-level effect estimates are computed, communicated, and updated over time.
- Advise on A/B Testing and Factorial Designs: Consult on experiment design for population-level tests. Provide guidance on power, sample size, randomization, and the interplay between individual and group-level inference.
- Partner with the AI/ML Team: Work directly with ML Engineers on the recommendation and intervention system architecture, aligning taxonomy structure and causal-inference methodology with the technical architecture of the system.
- Advise on Methodology: Consult on measurement design, study methodology, and intervention evaluation frameworks as needed. Serve as an internal expert reference for the BeSci team.
- Collaborate with the Behavioral Analyst: Partner with the Behavioral Analyst to ensure the taxonomy and statistical learning framework are aligned with the behavioral theory driving intervention selection.
Qualifications
- PhD in Behavioral Science, Health Psychology, Behavioral Medicine, Data Science, Machine Learning, or a closely related field with publication track record; advanced training or applied experience in causal inference, reinforcement learning, or related quantitative discipline is required.
Skills
Required:Just-in-time adaptive intervention (JITAI) system design
Benefits
- 100% covered Medical/Dental/Vision insurances for employee AND spouse + dependents
- 401K with 4% employer match (eligible after 90 days of employment) and immediate 100% vesting
- Generous PTO policy + paid holidays
- Life Insurance
- Voluntary Life Insurance
- Disability Insurance
- Critical Illness Coverage
- Accident Insurance
- Healthcare FSA
- Dependent Care FSA
- Travel Assistance Program
- Employee Assistance Program (EAP)
- Fully stocked kitchen
Last updated Oct 21, 2026. Interested? Apply on the company site.
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