Sr. Applied Behavioral Scientist

About the Company

VeSync is a portfolio company with brands covering different categories of health & wellness products. You may already own a Levoit air purifier or a COSORI air fryer. We’re a young, energetic company with tremendous success and constant growth, recognized by awards from CES Innovation, iF Design, IGA, and Red Dot. Our teams are smart, diligent, and take ownership of their work while collaborating with open ears and a spirit of learning. If you’re down-to-earth, approachable, and easy to converse with, this may be a great fit.

Explore our brands: levoit.com | cosori.com | etekcity.com

About the Role

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 closely intertwined functions: 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.

Responsibilities

• Just-in-time Adaptive Intervention System Support
  • 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 (BCTTv1, COM-B, and relevant behavior change evidence) into a practical classification system that Engineering and ML teams can operationalize.
  • Review and validate behavioral logic and algorithms, flagging areas where the system’s behavioral logic deviates from the evidence base.
  • Maintain publication-grade evidence standards, helping 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 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 and tailoring variables (behavioral state, context, receptivity, and prior response history) that inform each decision.
  • Design decision rules from behavioral evidence, translating BCTTv1, COM-B, and relevant behavior change evidence into rules that map tailoring-variable values to specific intervention options.
  • Own the behavioral taxonomy and delivery constraints, including rules for cadence, cooldowns, and sequencing to protect against message fatigue and habituation.
  • Review, validate, and maintain evidence standards for decision rule performance in production, flagging deviations from the evidence base.
• Causal Inference & Learning Loop
  • Own the learning loop attribution scheme: design the statistical and causal framework for the Learning Loop before product launch, specifying micro-randomization strategies, off-policy evaluation methods, and individual-level effect estimation approaches.
  • Define instrumentation requirements by partnering with Engineering to specify event logging and data infrastructure needed to support the learning loop.
  • Specify adaptive trial designs, 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 by developing the analytical framework for N-of-1 self-experiments within the product, defining how individual-level effect estimates are computed, communicated, and updated over time.
  • Advise on A/B testing and factorial designs, providing guidance on power, sample size, randomization, and the interplay between individual and group-level inference.
• Cross-Functional Leadership
  • Partner with the AI/ML Team on the recommendation and intervention system architecture, aligning taxonomy structure and causal-inference methodology with the technical architecture of the system.
  • Advise on methodology, including measurement design, study methodology, and intervention evaluation frameworks, serving as an internal expert reference for the BeSci team.
  • Collaborate with the Behavioral Analyst to ensure the taxonomy and statistical learning framework align with the behavioral theory driving intervention selection.

Requirements

• 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 combining 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: Ability to operate 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 skills. 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.

Location

Tustin, CA

Pay

Starting at $200K annually

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