Robotics Automation Data Scientist

Data Scientist – Business Intelligence & Operational Optimization Position Summary

The Data Scientist – Business Intelligence & Operational Optimization is responsible for transforming operational data into actionable insights that improve the performance, efficiency, and scalability of robotic warehouse systems.

This role combines business intelligence, advanced analytics, and optimization techniques to identify opportunities for improving warehouse throughput, robot utilization, inventory flow, order fulfillment performance, and overall system effectiveness. The position works closely with Product Management, Engineering, Robotics, Operations, and Customer Success teams to establish data-driven decision‑making processes and continuously improve customer outcomes.

The ideal candidate possesses strong analytical skills, experience with large operational datasets, and a passion for solving complex logistics and automation challenges.

• Develop and maintain operational dashboards and reporting platforms.
• Define and monitor key performance indicators (KPIs) across customer deployments.
• Create executive, operational, and customer-facing performance reporting.
• Analyze trends, anomalies, and performance degradation across warehouse operations.
• Translate complex operational data into actionable business recommendations.

• Analyze warehouse workflows to identify bottlenecks and inefficiencies.
• Develop recommendations to improve:
  • Warehouse throughput
  • Robot utilization
  • Inventory flow
  • Pick productivity
  • Station efficiency
  • Order cycle time
  • Resource allocation
  • Labor productivity
• Evaluate the impact of software releases and operational changes on customer performance.
• Measure system-wide performance and identify optimization opportunities.

• Analyze robotic fleet performance and utilization patterns.
• Identify congestion points and traffic inefficiencies.
• Evaluate task assignment effectiveness.
• Measure robot travel efficiency and idle time.
• Develop recommendations to improve fleet coordination and throughput.

• Build predictive and diagnostic analytical models.
• Develop forecasting models for:
  • Order volume
  • Inventory demand
  • Capacity planning
  • Resource utilization
• Support operational planning through scenario analysis.
• Create models that enable proactive issue identification.

• Design and analyze A/B tests and operational experiments.
• Measure effectiveness of software enhancements and workflow changes.
• Partner with Product and Engineering teams to validate hypotheses using data.
• Quantify ROI for optimization initiatives.

• Collaborate with engineering teams to improve data collection and instrumentation.
• Define operational metrics and data standards.
• Ensure data quality, consistency, and reliability.
• Support development of data pipelines and analytics infrastructure.

• Analyze customer deployment performance.
• Produce operational health assessments.
• Identify opportunities for customers to improve utilization of automation systems.
• Support customer success and deployment teams with performance analysis.

This role is expected to drive measurable improvements in:

• Orders processed per hour
• Inventory movement efficiency
• Pick rates
• Put rates
• Cycle count accuracy
• Order completion times

• Robot utilization
• Travel efficiency
• Congestion reduction
• Queue management
• Task completion rates
• Fleet balancing

• Workflow latency
• Service response times
• Throughput capacity
• Resource consumption
• Exception rates
• Operational uptime

• Bachelor's degree in Data Science, Statistics, Mathematics, Computer Science, Industrial Engineering, Operations Research, or related field.
• 3+ years of experience in data analytics, business intelligence, data science, or operational optimization.
• Strong SQL skills and experience working with large datasets.
• Strong proficiency in Java or Kotlin.
• Experience with streaming data (Kafka/MQTT).
• Experience developing dashboards and performance reporting.
• Strong statistical analysis and problem‑solving skills.
• Ability to communicate technical findings to business and operational stakeholders.

• Experience in logistics, supply chain, warehouse automation, robotics, manufacturing, or industrial operations.
• Experience with optimization, simulation, or operations research techniques.
• Familiarity with warehouse execution systems (WES), warehouse management systems (WMS), or robotics platforms.
• Experience with predictive analytics and machine learning.
• Experience working with time‑series operational data.
• Knowledge of queuing theory, scheduling, routing, or resource optimization.