Data Scientist — Agent Evaluations & Quality

About the Role

This company is building an AI executive assistant that operates across email, calendars, meetings, and business software. As a Data Scientist — Agent Evaluations & Quality, you will own the measurement system that determines whether the assistant is genuinely improving in ambiguous, real-world environments. You'll partner directly with AI Agent Capabilities engineers to generate the evidence that shapes product decisions, model choices, and release quality.

This is a high-ownership, deeply technical role at the intersection of applied data science, LLM evaluation, and product quality — ideal for someone who thrives on turning hard, open-ended quality questions into rigorous, actionable answers.

What You'll Do

• Architect and maintain automated evaluation pipelines that measure agent quality across product surfaces.

• Translate agent capabilities into explicit pass, partial-pass, and failure criteria for complex multi-step tasks.

• Build representative gold datasets and regression suites covering real workflows, edge cases, and adversarial scenarios.

• Define meaningful metrics — task success, tool-selection accuracy, instruction adherence, factual consistency, latency, cost, and reliability.

• Design deterministic and model-based graders, calibrate LLM-as-a-judge systems, and track grader agreement.

• Compare models, prompts, and implementations using rigorous offline experiments and production evidence.

• Analyze traces and production outcomes to identify root causes and build a practical failure taxonomy.

• Turn production failures into regression cases and continuously close gaps in evaluation coverage.

• Build dashboards and release-quality signals that make results actionable for engineering, product, and leadership.

• Recommend improvements to capability engineers and verify that fixes raise quality without unacceptable regressions.

What We're Looking For

Required

• 4+ years in Applied Data Science or Machine Learning roles, with a track record of building and delivering evaluation systems, automated data pipelines, or production ML infrastructure.

• Experience designing and implementing automated evaluation frameworks, success criteria, and regression suites for complex AI/ML or agentic systems.

• Production-grade proficiency in

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