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 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.
- What makes you a great fit
- You're product-oriented — you prioritize metrics tied to real user outcomes, not just convenient measurements.
- You drive ambiguous quality questions from evaluation design all the way into product decisions.
- You write maintainable, production-quality code — not just ad-hoc notebooks.
- You collaborate naturally with engineers and are comfortable digging into traces and system internals.
Location
- This role is on-site . Visa sponsorship is not available for this position.
Compensation & Benefits
- Compensation details were not provided for this listing. A competitive package commensurate with experience is expected at this stage of company growth.
Originally posted on Himalayas
