Senior Data Scientist

Traba is the AI operating layer for the industrial supply chain. We started in workforce—temp staffing, the biggest operational pain point for the manufacturing and logistics customers we serve—and used it to embed ourselves inside their daily operations and create a far better customer experience through technology. Now those same customers are pulling us beyond staffing into the broader operational workflows that run their facilities. That foundation gave us proprietary data from millions of shifts and deep enterprise relationships. But our edge is more than data: by connecting to the systems running across every facility and activating the workers already on our platform to execute against them, we are building applied AI that drives real productivity gains and transforms how the global supply chain operates at scale. We are backed by Founders Fund, Khosla Ventures, and General Catalyst. Traba is hiring a Senior Data Scientist to join the founding Agents team and lead measurement and modeling for our agentic platform from 0→1. You’ll make the core calls on how agent quality is defined, measured, and improved; set the bar for statistical and scientific rigor; and build the evaluation, experimentation, and modeling foundations that every agent we ship is measured against. As a Senior Data Scientist at Traba, you’ll own how we model and understand agent performance inside real customer workflows—capability, reliability, and unit economics—and partner with engineering, product, and operations leadership on the decisions that shape the platform. 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. - Provide Operations teams with models, self-service analytics, and advanced technologies—including AI-assisted tools—enabling them to independently analyze operational data and optimize their daily activities. - 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.