About the job
As the Lead Data Scientist, you'll define how we use data to build better robots and make better decisions. This is a high-impact, high-visibility role where you'll set the technical direction for analytics and modeling while helping build a truly data-driven engineering organization. In this newly created role, you'll transform massive volumes of complex robot data into the insights and models that drive decisions across hardware, software, manufacturing, and operations.
Responsibilities
Build models that predict MTBF and remaining useful life for specific components (actuators, cameras, compute, power systems).
Analyze telemetry and logs to find which software versions, site conditions, or usage patterns correlate with the highest failure and intervention rates.
Build detection and RCA tooling that surfaces anomalies in fleet behavior early and helps engineers get from symptom to cause faster.
Join end-of-line test data with field performance to find which manufacturing signals predict early field failures, and close the loop back to the factory to catch defects before they ship.
Qualifications
Minimum
10+ years applying data science / statistical modeling to real-world problems, with a track record of owning ambiguous, high-impact problems end to end.
Deep expertise in some combination of: reliability/survival analysis, time-series and anomaly detection, predictive maintenance, and causal/observational inference.
Strong software fundamentals — production-quality Python, comfort in SQL and modern data stacks.
3+ years serving as a technical lead or the senior-most IC on cross-functional efforts, with a track record of setting technical direction for a team of data scientists/analysts, mentoring and growing ICs, and driving alignment across engineering and business stakeholders without formal authority
Fluency partnering cross-functionally with hardware, embedded/software, manufacturing, and business/finance stakeholders, and translating analysis into decisions they trust.
Experience working with large-scale, messy, multi-modal operational data (sensor/telemetry, logs, event streams) and the judgment to know when the data can and can't support a conclusion.
Authorization to work in the USA
Preferred
Prior work in robotics, autonomous systems, hardware, IoT/connected devices, industrial, or manufacturing settings.
Familiarity with fleet operations, RaaS/subscription unit economics, or SRE-style operational metrics.
Exposure to manufacturing quality systems (end-of-line test, SPC, yield/defect analytics).