Senior Data Scientist

About the role

The Senior Data Scientist will lead measurement and modeling for Traba’s agentic platform, providing strategic insights and recommendations to senior leadership through in-depth statistical analysis and modeling.

Responsibilities

• Provide strategic insights and recommendations to senior leadership through in-depth statistical analysis and modeling.
• Design, build, and maintain the metrics, models, and reporting that track agent quality, reliability, adoption, and unit economics for stakeholders across the Agents and Operations teams.
• Build evaluation and experimentation as a first-class discipline—datasets from production traces, rubrics, automated graders, regression suites, and the experiment design and analysis that prove causation—so every agent improvement we ship is backed by evidence.
• Identify key business challenges and opportunities—including agent failure modes, tool-use patterns, and cost and latency—and build statistical and machine-learning models to drive product improvements and growth initiatives.
• Architect scalable analytics and modeling infrastructure to ensure data integrity, governance, and accessibility for both human and agent consumers.
• Oversee the development and maintenance of Traba’s data warehouse to ensure data availability and governance.
• Work closely with the Agents team and Operations leadership to understand their data needs and provide actionable, statistically grounded insights that drive continuous process improvement and operational efficiency.
• Mentor the scientists and analysts who build alongside you, and set the standards that define what “good” looks like for measurement, modeling, and experimentation at Traba.

Qualifications

• Experience: 4-8 years in data science, machine learning, applied statistics, or quantitative research, with 2+ years of hands-on work modeling or measuring LLM- or agent-based systems in production.
• Education: BS/MS/PhD in data science, statistics, machine learning, computer science, mathematics, economics, or a related quantitative field (or equivalent work experience).
• Technical Skills: Strong proficiency in Python and common ML and statistics libraries (e.g., scikit-learn, PyTorch, pandas, statsmodels). Strong proficiency in SQL. Experience designing and analyzing experiments (A/B testing) and applying statistical inference or causal methods. Experience with LLM evaluation and observability tools like Langfuse, Braintrust, or internal harnesses, and with building automated evaluators.
• Communication Skills: Excellent data storytelling skills to effectively engage with stakeholders.
• Collaboration Skills: Strong ability to work across departments, identifying and prioritizing analytics problems to deliver actionable insights.
• Curiosity and Initiative: Intense curiosity to ask “why?” and use data to find answers, combined with a “no task too small” mentality.
• Self-Motivation: Ability to work independently and as part of a team in a fast-paced startup environment.

Bonus Skills

• Experience with notebook tools like Jupyter, Hex, Hyperquery, or equivalent.
• Experience with modern data stack tools like dbt or equivalent.
• Experience building internal agents or MCP servers for analytics workflows, or prior work at a vertical AI or AI-native data company (e.g., Hex, Omni, dbt).
• Experience fine-tuning, distilling, or rigorously evaluating LLMs, or applying causal inference and experimentation at scale.

Benefits

• Start-up equity
• Competitive Salary
• 100% Paid health, dental & vision coverage
• Dinner Provided via DoorDash, free DashPass & stocked kitchen for NY employees
• Commuter benefit
• GYMPASS Benefit

Salary Range Details

The compensation range for this position is set between $180,000 and $215,000, reflecting our market analysis and other relevant considerations. However, exceptions may be made for candidates with qualifications that significantly differ from those outlined in the job description. We also offer a highly competitive equity package designed to ensure you share meaningfully in the long-term success and upside of the business.