Lead Data Scientist (Agentic AI) jobs in United States
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Jecona · 12 hours ago

Lead Data Scientist (Agentic AI)

Jecona is partnering with a company that is building a fully agentic social dating platform where AI manages user preferences and matches. They are seeking a Lead Data Scientist to design and oversee the entire data science process, focusing on creating systems that improve learning and adaptability over time.

Management Consulting
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Hiring Manager
Jeff Chan
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Responsibilities

Design and own the entire end to end data science process
Build systems that capture continuous experience streams, transform signals into rewards grounded in real outcomes, feed those rewards back into agents, prevent drift while still allowing improvement, help the system reason and plan based on consequences
Own the full loop: data → experience → reward → feedback loops → discovering leverage points → adaptation → product outcomes
Architect the data engine that powers experiential learning
Design reinforcing loops that compound value responsibly
Design balancing loops that stabilize trust, fairness, and safety
Identify and avoid system traps (gaming metrics, tragedy-of-the-commons patterns)
Push on leverage points that change behavior — not just parameters

Qualification

Applied ML / Data ScienceBuilding LLM-enabled systemsBehavioral pipelinesFeedback & reward loopsLarge-scale data engineeringLangGraphLangChainReward shapingValue estimationWorld modelingTemporal / TD learningLong-horizon feedback loopsSocial graphsMatchmaking systemsRecommendation systemsTrust & safetyAnomaly detectionCausal inferenceReinforcement learning

Required

10+ years in applied ML / data science (production)
3+ years building LLM-enabled systems
built behavioral pipelines that drive real agent / product behavior
designed feedback & reward loops end-to-end
hands-on large-scale data engineering
deep, practical experience with agent frameworks, including: LangGraph (preferred), LangChain, or equivalent agent-orchestration frameworks in production
experience feeding data back into agents to actually change behavior
strong grounding in: reward shaping, value estimation, world modeling, temporal / TD learning, long-horizon feedback loops

Preferred

social graphs, matchmaking, recommendation systems
trust & safety, anomaly detection, abuse prevention
causal inference / world-model thinking
reinforcement learning or TD-style learning
experience grounding rewards in real outcomes, not proxy metrics

Company

Jecona

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At Jecona, we work with companies on an exclusive and retained basis within the skilled trade and technical (AI, ML, Software, Data, Tech Sales) space.

Funding

Current Stage
Early Stage
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