Senior Data Scientist

Job Description

Job Description

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 productionize 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 definition through measurable business impact. 
• A quantitative degree in computer science, statistics, mathematics, operations research, economics, engineering, physics, or a related field; advanced degree preferred, not required.

The starting cash range for this role is $160,000-220,000. Please note that this is a target, starting cash range for a candidate who meets the minimum qualifications for this role. The final cash pay for this role will depend on a variety of factors, including a specific candidate's experience, qualifications, skills, working location, and projected impact. The total compensation package for this role may also include: equity compensation; overtime pay; discretionary annual or performance bonuses; sales incentives; benefits such as medical, dental and vision insurance; paid time off; and more.

Zipline is an equal opportunity employer and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws or our own sensibilities.

We value diversity at Zipline and welcome applications from those who are traditionally underrepresented in tech. If you like the sound of this position but are not sure if you are the perfect fit, please apply!

Voluntary Self-Identification

For government reporting purposes, we ask candidates to respond to the below self-identification survey. Completion of the form is entirely voluntary. Whatever your decision, it will not be considered in the hiring process or thereafter. Any information that you do provide will be recorded and maintained in a confidential file.

As set forth in Zipline 's Equal Employment Opportunity policy, we do not discriminate on the basis of any protected group status under any applicable law.