Senior Data Scientist

Zipline; San Francisco (CA); On-site; USD 180,000 - 240,000; Full time

Job summary

Zipline is building an autonomous delivery service for fast, reliable real-world operations. A Senior Data Scientist on the Data Science & Analytics team will own high-leverage marketplace and operational decisions from framing through launch and iteration.

You will turn ambiguous questions into models, experiments, simulations, tools, and recommendations for product, engineering, operations, and business teams to scale the network, including pricing, routing, fleet utilization, charging

Qualifications

• 6+ years in data science, ML, statistics, OR, economics or related analytical field.
• Experience applying ML/optimization/causal inference to product or business problems.
• Experience designing and interpreting experiments, including A/B tests.
• Ability to build explainable models and tools driving measurable impact.

Responsibilities

• Own high-leverage marketplace and operational decision problems from framing through launch and iteration.
• Build models, simulations, and data products for pricing, routing, fleet utilization, and network planning.
• Design and analyze A/B tests, quasi-experiments, and causal studies to evaluate features.
• Partner with engineers to productionize models and experimentation infrastructure.
• Communicate findings to technical and non-technical stakeholders and drive decisions.
• Monitor model performance post-launch and iterate with operating teams.

Skills

Marketplace experience

Logistics domain

Pricing & routing

Experimentation design

Stakeholder communication

Ambiguity framing

Python

SQL

Productionizing models

Analytical problem solving

Education

Quantitative degree (CS/Stats/Math/OR/Economics/Engineering/Physics)

Tools

Python

SQL

C/C++

Go

Job description

• 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

• Experience with marketplace, logistics, mobility, delivery, pricing, routing, fleet management, or similarly complex operational systems
• 6+ years of experience in data science, machine learning, applied statistics, operations research, economics, or a related analytical field
• A quantitative degree in computer science, statistics, mathematics, operations research, economics, engineering, physics, or a related field; advanced degree preferred, not required
• 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
• Ability to build usable, explainable models and tools and to drive work from problem definition through measurable business impact