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.

• 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 Python and SQL, with experience building and maintaining automated analytical pipelines on large datasets.
• Applied statistical and experimental skills: significance testing, variance analysis, and sampling to evaluate non-deterministic AI/ML systems.
• Experience developing labeled datasets, annotation guidelines, and quality-control processes for ground-truth data in dynamic product environments.
• Solid understanding of LLM agent behaviors: tool use, multi-step execution, retrieval, and practical failure modes.
• Demonstrated ability to analyze model traces, tool calls, and outputs to identify root causes across model, prompt, tool, and data layers.
• Experience using production telemetry and observability data to monitor system quality, build dashboards, and analyze real-world user outcomes.
• Nice to Have
• Hands-on experience with LLM-as-a-judge systems, model-based grading, or AI benchmarking platforms.
• Experience shipping or operating production ML products, agentic systems, or customer-facing consumer software.
• Experience reviewing and adapting public research benchmarks or academic evaluation methodologies to real-world product problems.

• SQL
• Research
• Machine learning
• Quality control
• Python
• Leadership
• Dashboards

Clera