As a Senior Data Scientist on the Data Science & Analytics team at Zipline, 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. Your work will focus on pricing, routing, fleet utilization, charging strategy, experimentation, and network planning to support the autonomous delivery service.

Responsibilities

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.

Requirements

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.