RemoteFull timeMid level$140k – $210kPosted today
Apply with JobAssistAbout the role
- 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 decisions
- Ability to explain complex technical concepts to business stakeholders
- This role is ideal for someone who enjoys solving real-world operational problems with advanced analytics and building models that directly influence business outcomes
- Familiarity with geospatial analytics and network optimization
- Experience with optimization techniques such as linear programming, mixed-integer optimization, simulation, or network modeling
- Strong proficiency in Python
- Experience with cloud data platforms and modern data stacks
- Strong knowledge of statistical inference and experimental design
- Advanced SQL skills
- Experience deploying models into production environments
- 3–7 years of experience in Data Science, Operations Research, Analytics, Applied Statistics, Industrial Engineering, or a related field
- Experience in logistics, transportation, aviation, robotics, autonomous systems, manufacturing, or supply chain operations
- Experience with machine learning, statistical modeling, forecasting, and experimentation
- Master’s or PhD in Statistics, Operations Research, Industrial Engineering, Applied Mathematics, Computer Science, or related disciplines
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