- As an Operations Data Scientist, you will develop the predictive models, optimization algorithms, and analytical frameworks that drive operational decision-making across our global network
- You will work at the intersection of data science, operations research, logistics, and business strategy to improve network performance, forecast demand, optimize resource allocation, and identify opportunities to scale efficiently
- Develop forecasting models for operational demand, capacity, labor requirements, inventory, and network utilization
- Build optimization models that improve resource allocation, staffing, maintenance planning, and operational efficiency
- Design and analyze experiments to evaluate operational initiatives and process changes
- Create predictive models that identify operational risks, bottlenecks, quality issues, and reliability trends
- Develop simulation models to evaluate network expansion scenarios and operational strategies
- Partner with Operations, Engineering, Product, Supply Chain, and Finance teams to guide strategic decisions
- Build production‑ready analytical tools and data products used by operational teams
- Establish advanced operational metrics and measurement frameworks
- Communicate complex analytical findings to technical and non‑technical stakeholders
- Support long‑term planning through scenario analysis and decision modeling
Example Problems You’ll Solve
- How many technicians will be required at each site over the next 12 months?
- What maintenance strategy minimizes downtime while controlling cost?
- How should inventory be positioned across the network to maximize service levels?
- Which operational variables best predict delays, failures, or quality issues?
- Where should future sites be launched to maximize network efficiency?
- What is the optimal staffing model for a rapidly scaling operation?
Success Metrics
- Forecast accuracy
- Measurable operational cost savings
- Capacity utilization improvements
- Reduction in operational disruptions
- Adoption of predictive and optimization tools by operational teams
- Strategic decisions influenced by analytical recommendations
Experience building analytical solutions that influence operational decisionsAbility to explain complex technical concepts to business stakeholdersThis role is ideal for someone who enjoys solving real‑world operational problems with advanced analytics and building models that directly influence business outcomesFamiliarity with geospatial analytics and network optimizationExperience with optimization techniques such as linear programming, mixed‑integer optimization, simulation, or network modelingStrong proficiency in PythonExperience with cloud data platforms and modern data stacksStrong knowledge of statistical inference and experimental designAdvanced SQL skillsExperience deploying models into production environments3–7 years of experience in Data Science, Operations Research, Analytics, Applied Statistics, Industrial Engineering, or a related fieldExperience in logistics, transportation, aviation, robotics, autonomous systems, manufacturing, or supply chain operationsExperience with machine learning, statistical modeling, forecasting, and experimentationMaster’s or PhD in Statistics, Operations Research, Industrial Engineering, Applied Mathematics, Computer Science, or related disciplines
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