We’re hiring a Lead Data Scientist (Data Engine) to design and own the learning backbone that allows Ditto’s agents to improve safely over time.

You will build systems that:

capture continuous experience streams, not snapshots 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, not guesswork This role owns the full loop: data → experience → reward → feedback loops → discovering leverage points → adaptation → product outcomes

You are here to build a living system that learns — continuously — and to identify small, high-leverage changes that create outsized impact over time.

You will architect the data engine that powers experiential learning:

experience streams — behavior stitched across long time horizons reward streams — derived from actions, outcomes, and environment feedback models that capture intent, preference, habit, and change evaluation pipelines that measure long-term improvement, not one-off wins matchmaking & recommendation signals that uncover hidden compatibility systems that let agents plan based on predicted consequences experimentation frameworks (A/B tests, bandits, sequential testing) drift detection & safety monitors guardrails to prevent reward hacking, bias loops, or unintended behaviors Everything must be auditable, grounded, explainable, repeatable.

You will:

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 Sometimes the right move is not tuning a metric — it’s redefining the goal.