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Senior Staff Data Scientist, Robotics

Agility Robotics· Software Engineering
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Hybrid (Salem, OR / Pittsburgh, PA / Fremont, CA)$218k - $340kfull-timePosted 2mo ago

About the role

Senior Staff Data Scientist responsible for defining how data informs robot design, reliability, and fleet operations for Digit humanoid robots deployed in RaaS. High-impact, high-visibility role shaping analytics strategy, modeling, and data-driven decision making across hardware, software, manufacturing, and operations.

Responsibilities

  • Predictive maintenance & hardware reliability: build models for MTBF and remaining useful life for components; collaborate with hardware engineering to model wear-and-tear under varying duty cycles, payloads, and environmental conditions; translate models into maintenance schedules and design feedback.
  • Fleet performance & RaaS unit economics: analyze telemetry/logs to identify correlations with failures/interventions; define golden signals for RaaS deployments; build dashboards; quantify cost of human intervention and drivers.
  • Root-cause & anomaly detection tooling: build detection and RCA tooling to surface anomalies early and expedite diagnosis.
  • Manufacturing quality & feedback loops: join end-of-line test data with field performance to predict early field failures and close the loop to manufacturing to prevent defects.
  • Experimentation & fleet A/B: framework for safely rolling out software/firmware changes across a physical fleet and measuring impact.
  • Data quality & instrumentation strategy: define logging fidelity and data requirements with embedded/software teams.
  • Demand/capacity & deployment economics: models for fleet sizing, spares/inventory, and new site rollouts.
  • Safety statistics.

Qualifications

  • 10+ years applying data science / statistical modeling to real-world problems, with end-to-end ownership of ambiguous, high-impact problems.
  • MS or PhD in Statistics, Computer Science, Operations Research, Engineering, Physics, or related quantitative field; PhD with relevant coursework/experience in reliability engineering, survival analysis, or applied statistics preferred.
  • Prior work in robotics, autonomous systems, hardware, IoT/connected devices, industrial, or manufacturing settings.
  • Deep expertise in reliability/survival analysis, time-series and anomaly detection, predictive maintenance, and causal/observational inference.
  • Strong software fundamentals — production-quality Python, SQL and modern data stacks.
  • 3+ years as a technical lead or senior IC on cross-functional efforts, setting technical direction and mentoring.
  • Experience partnering cross-functionally with hardware, embedded/software, manufacturing, and business/finance stakeholders.
  • Experience with large-scale, multi-modal operational data (sensor/telemetry, logs, event streams) and judgment about data sufficiency.
  • Authorization to work in the USA

Skills

Required:Data science leadershipReliability analysisTime-series / anomaly detectionPredictive maintenance

Benefits

  • 401(k) Plan with 6% company match
  • Equity: company stock options
  • 100% company-paid medical, dental, vision, and short/long-term disability insurance
  • Well-Being Support: Employee Assistance Program (EAP)
  • Flexible, unlimited PTO (exempt) / 10 vacation days + paid sick leave + 12 holidays (non-exempt)
  • Catered lunches four times a week and snacks at Salem and Pittsburgh offices
  • Generous paid parental leave programs
  • Flexible work arrangements culture
  • Professional development and tuition reimbursement
  • Relocation assistance for eligible roles
  • Annual discretionary bonus (eligible roles)
Last updated Aug 20, 2026. Interested? Apply on the company site.

About Agility Robotics

Industrial Humanoid Automation | Agility

Industrial Humanoid Automation | Agility's humanoid robots are deployed today in manufacturing, distribution, and logistics. Proven automation for industrial environments.

ArcArms and roboticsAutomationDigitHumanoid robotsIndustrial automationcloud platformdistribution
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