[Hiring] Staff Data Scientist @Yalo Inc.

Role Description

As a Staff Data Scientist, you make our AI agents measurably better, and you prove it. You own how Yalo evaluates agents: evals, trace and error analysis, and the improvement loop that turns what we find in production into fixes to our agent skills, harness, CLI and customers' conversational flows. You work with Product, Engineering, Delivery and Customer Success. Your job is to show what moved the metric, not to guess.

Responsibilities

• Own the evals strategy for our agents: pre-release behavioral testing, production monitoring, and the judges that score conversations.
• Run continuous error analysis on agent traces at scale. Find failure patterns, attribute them to skills, prompts or releases, and rank improvement opportunities by impact.
• Close the loop. Turn findings into fixes and new evals, ship them, and measure the result with sound before-and-after and causal methods.
• Improve our agent skills, harness and CLI using trace evidence. Work through the owning teams' repos and review process.
• Partner with delivery and customer teams to take real customer flows from underperforming to reliably healthy, and make the process repeatable for others.
• Calibrate and validate LLM judges against human review. Measure their stability, report their limits, and set the bar for what counts as evidence at Yalo.
• Write production-quality Python and TypeScript for evals and analysis tooling, with tests and CI, so it can live in a shared codebase.
• Mentor data scientists and engineers on measurement, experimentation and evaluation practice.
• Help shape quarterly and annual planning for agent quality.

Qualifications

• BS or MS in a technical field (Computer Science, Statistics, Applied Math, Operations Research, Economics, etc.).
• 8+ years delivering data science solutions with measurable business impact in cross-functional teams.
• Hands-on experience evaluating LLM-based systems or agents: eval design, LLM-as-judge, rubric design, and judge calibration.
• Expert in Python and SQL. Comfortable in TypeScript, or ready to be.
• Strong production engineering habits: version control, code review, testing, CI/CD, and shipping tools others use.
• Experience with GenAI systems in production: prompt and tool design, agent frameworks (e.g., LangGraph), and LLM observability tooling (e.g., LangSmith).
• Skilled at turning messy trace and conversation data into clear findings for technical and non-technical audiences.
• Strong product and business sense. You frame quality work in terms of customer outcomes.

Requirements

• Experience with conversational AI, customer-facing agents or tool-using agents.
• NLP and clustering for failure-pattern discovery.
• Causal inference and experimentation skills: A/B tests, difference-in-differences, pre/post analysis, and the judgment to say what the data can't show.
• Exposure to MLOps or LLMOps platforms (Vertex, SageMaker, MLflow, or similar) and big data tools.
• Familiarity with AI safety and responsible AI practices.
• Comfort in an ambiguous, fast-changing environment.
• Experience mentoring other data scientists.

Benefits

• Unlimited PTO policy
• Competitive rewards on the market range
• Remote working is available (-+3 hours CT)
• Flexible time (driven by results)
• Start-up environment
• International teamwork
• You and nothing else limit your career here