Operations Data Scientist – Strategic Operations

About this role

About Zipline

Zipline is the world’s largest and most experienced drone delivery service. We are on a mission to serve all humans equally by ensuring access to food, medicine and essential goods anytime, anywhere. We design, build, and operate the world’s largest autonomous logistics system, delivering critical supplies quickly and reliably. Today, Zipline operates on four continents, makes a delivery somewhere in the world every 30 seconds, and has completed millions of deliveries to date, including blood, vaccines, medical supplies, food, and retail products.

Our customers include the world’s largest and most prominent healthcare systems, governments, retailers, restaurants and global businesses who rely on us to save lives, reduce emissions, increase economic opportunity, and provide delivery from point A to point B as fast as possible. The drone is only 15% of what we’ve built to enable seamless, reliable, global operations.

Our system strengthens supply chains, reduces congestion, and gives people time back. With more than 140 million commercial autonomous miles safely flown, Zipline is redefining access to healthcare, consumer products, and food across the globe.

We operate at a global scale and are looking for practical problem solvers who thrive on real-world challenges and rapid growth. Our team is motivated by building systems that have a direct, meaningful impact on people’s lives and by scaling the future of logistics. We are seeking people who sculpt from first principles, enjoy facing adversity, and can do the impossible at record breaking speeds.

About 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.

This role is ideal for someone who enjoys solving real-world operational problems with advanced analytics and building models that directly influence business outcomes.

What You'll Do

• 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.

Required Qualifications

• 3–7 years of experience in Data Science, Operations Research, Analytics, Applied Statistics, Industrial Engineering, or a related field.

• Strong proficiency in Python.

• Advanced SQL skills.

• Experience with machine learning, statistical modeling, forecasting, and experimentation.

• Experience with optimization techniques such as linear programming, mixed-integer optimization, simulation, or network modeling.

• Strong knowledge of statistical inference and experimental design.

• Experience building analytical solutions that influence operational decisions.

• Ability to explain complex technical concepts to business stakeholders.

Preferred Qualifications

• Experience in logistics, transportation, aviation, robotics, autonomous systems, manufacturing, or supply chain operations.

• Experience deploying models into production environments.

• Experience with cloud data platforms and modern data stacks.

• Familiarity with geospatial analytics and network optimization.

• Master's or PhD in Statistics, Operations Research, Industrial Engineering, Applied Mathematics, Computer Science, or related disciplines.