Senior Data Scientist

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.

Zipline is building an autonomous delivery service that helps goods move quickly and reliably through the real world. As the service scales, decisions about pricing, routing, fleet utilization, charging, network planning, and customer experience must work across software, vehicles, and field operations. As a Senior Data Scientist on the Data Science & Analytics team, you will own high‑leverage marketplace and operational decision problems from framing through launch and iteration.

You will turn ambiguous questions into models, experiments, simulations, tools, and recommendations that product, engineering, operations, commercial, and business teams can use to scale the network.

• Lead analysis and decision systems for priority domains including pricing, routing, fleet utilization, charging strategy, experimentation, and network planning.
• Frame ambiguous business and product questions, select the right analytical approach, and make clear recommendations with tradeoffs and expected impact.
• Build machine learning, statistical, optimization, forecasting, and simulation models for marketplace, operational, and product decisions.
• Design and analyze A/B tests, quasi-experiments, causal studies, and metric frameworks to evaluate features, policies, and growth interventions.
• Partner with engineers to product ionize models, decision systems, data products, and experimentation infrastructure.
• Own post‑launch decision quality by monitoring model and KPI performance, identifying drift or missed outcomes, and driving iterations with engineering and operating partners.
• Communicate complex findings to technical and non‑technical stakeholders, aligning teams on decisions that improve reliability, utilization, customer experience, and unit economics.

• 6+ years of experience in data science, machine learning, applied statistics, operations research, economics, or a related analytical field.
• Experience applying machine learning, statistical modeling, optimization, or causal inference to real‑world product or business problems.
• Strong Python, C/C++, or Go programming skills, plus SQL proficiency and experience working with large, complex datasets.
• Experience designing and interpreting experiments, including A/B tests, multiarm bandits, power analysis, metric design, and ambiguous causal results.
• Experience with marketplace, logistics, mobility, delivery, pricing, routing, fleet management, or similarly complex operational systems.
• Ability to build usable, explainable models and tools and to drive work from problem…