Sr. Applied Behavioral Scientist

Senior Applied Behavioral Scientist

VeSync is a portfolio company with brands that cover different categories of health & wellness products. We wouldn't be surprised if you have one of our Levoit air purifiers in your living room or a COSORI air fryer whipping up healthy and delicious meals for you every night.

We're a young and energetic company, we've had tremendous success, and we are constantly growing our team. As we garner more industry attention – just check out our accomplishments and awards by CES Innovation, iF Design, IGA, and Red Dot – we also need driven and talented people to join our team.

That brings us to you, and what you'll be joining. Our teams are smart and diligent and take ownership of their work – they're confident in their work but know how to collaborate with open ears and a spirit of learning. If you're down-to-earth, approachable, and easy to strike up a conversation with, this may be a great fit for you.

Check out our brands:

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• 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…