What you will do at VeSync:

Just-in-time Adaptive Intervention System

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.

Causal Inference & Learning Loop

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.

Cross-Functional Leadership

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.

What you bring to the role:

Education: PhD in Behavioral Science, Health Psychology, Behavioral Medicine, Data Science, Machine Learning, or a closely related field is required, with a strong publication track record demonstrating expertise in combining behavior change theory with closed-loop system design. Advanced training or applied experience in causal inference, reinforcement learning, or a related quantitative discipline is also required.
Experience: 5+ years in a senior behavioral science role that combines intervention design or taxonomy/framework development with causal-inference or quantitative research, ideally in health tech, digital health, or algorithmic decision-making (ADM) contexts.
Behavior Change Frameworks: Deep expertise in BCTTv1, COM-B, and related behavior change models, with prior experience applying these frameworks to digital health or technology-mediated interventions strongly preferred.
Causal Inference & Adaptive Methods: Demonstrated expertise in causal inference, off-policy evaluation, and adaptive trial designs. Hands-on experience with Micro-Randomized Trials (MRTs), Multi-Arm Bandits (MABs), and Reinforcement Learning (RL) algorithms in applied settings.
N-of-1 and Sequential Methods: Experience with N-of-1 experimental designs, sequential decision-making, and within-person inference. Comfort with the statistical challenges of small-n, high-frequency behavioral data.
Applied Comfort: Must be comfortable operating at an industry pace — translating academic rigor into actionable, sprint-compatible deliverables without sacrificing scientific integrity.
Technical Collaboration: Proven ability to work closely with engineering and ML teams on knowledge representation, taxonomy design, structured data schemas, and data instrumentation requirements.
Communication: Excellent written and verbal communication. Able to write clear methodology specs, taxonomy specifications, and review memos, and to present complex statistical and behavioral concepts to both technical and non-technical audiences.
Industry Knowledge: Experience in health tech, behavior change platforms, or consumer wearable/connected device ecosystems is strongly preferred.
Engagement Type: This is a full-time role. Candidates should be prepared to commit to a defined scope and timeline, with structured touchpoints across the Engineering, ML, and BeSci teams.

Location:

• Remote

Salary:

• Starting at $200K Annually

Perks and 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