Data Scientist / Senior Data Scientist

The Analytics team is looking for experienced Data Scientists and Senior Data Scientists to guide measurement, strategy, and tactical decision-making across the company across a variety of teams and levels. Data Scientists at DoorDash work to uncover insights and turn them into relevant recommendations, driving decisions for the entire organization. Analytics is integral to all operational areas at DoorDash.

The Storage teams build and operate online stateful systems and abstractions that are reliable, efficient, secure and easy to use for DoorDash Engineering. The teams are responsible for understanding Product Engineering’s evolving needs and developing platform and infrastructure capabilities to serve them. The team currently supports CockroachDB, Cassandra, Kafka and Redis as well as data abstraction services to reduce the complexity of interacting with storage systems for Product Engineers.

• Consumer & Growth
• Business Operations
• Dasher & Logistics
• Customer Experience & Integrity
• Merchant, Ads & Sales
• New Verticals
• International Data Science

As a Data Scientist at DoorDash, you'll use your quantitative background to mentor other scientists and dive into large datasets to guide decision-making. We solve a multitude of exciting challenges including customer acquisition, fraud and support, marketing, balancing supply and demand, new city launches, marketplace efficiency, and more. If you enjoy finding patterns amidst chaos, and have experience using analytics to affect revenue, growth, operations or beyond, we're looking for someone like you!

• Use quantitative analysis and the presentation of data to see beyond the numbers and understand what drives our business
• Build full-cycle analytics experiments, reports, and dashboards using SQL, R, Python, or other scripting and statistical tools
• Work with and mentor junior analysts on how to use more advanced methods and solve challenges
• Produce recommendations and use statistical techniques and hypothesis testing to validate your findings
• Provide insights to help business and product leaders understand marketplace dynamics, user behaviors, and long-term trends
• Identify and measure levers to help move essential metrics and make recommendations
• Work backwards from understanding and sizing problems to ideating solutions
• Report against our goals by identifying essential metrics and building executive-facing dashboards to track progress
• Collaborate with engineering to implement, document, validate, and monitor our logging

We’re hiring a Data Solutions Engineer with deep expertise in distributed databases, particularly Apache Cassandra, Redis, Kafka, and database agnostic abstractions. In this role, you will design, optimize, and scale distributed data access layers that power DoorDash’s most critical systems, ensuring high availability, low latency, and fault tolerance.

You’ll serve as a hands-on architect and technical partner to product engineering and infrastructure teams, helping translate complex business requirements into resilient and scalable data models. Your work will directly influence the evolution of Taulu, DoorDash’s unified storage abstraction layer, by shaping best practices and identifying platform gaps through real world engagements.

This is a high-impact, cross functional role that combines deep technical expertise with a customer centric approach. You’ll lead solutioning engagements from design through production, drive the adoption of Taulu modeling best practices, and ensure that our systems meet goals around reliability, cost efficiency, and velocity. You must be located in San Francisco, Sunnyvale, Seattle or New York for this hybrid opportunity. 

• Design and implement highly scalable, fault tolerant distributed database solutions using Taulu, Apache Cassandra, Redis, Kafka, and other paved path storage solutions. 
• Architect and optimize multi-region, globally distributed systems to meet our high standards for availability, latency, and throughput.
• Lead data modeling, performance tuning, and capacity planning for large-scale, mission-critical storage workloads.
• Partner with product engineering and infrastructure teams to deeply understand domain specific data needs and guide them in adopting paved path storage solutions.
• Serve as the DRI for solutioning engagements, owning modeling in Taulu from experimentation through launch and scale.
• Shape the evolution of Taulu by identifying abstraction gaps and converting customer feedback into platform improvements.
• Apply workload-aware design patterns, including caching strategies, partitioning, and consistency tuning to improve performance and efficiency.
• Drive adoption of operational best practices across observability, schema design, capacity planning, and cost optimization across storage systems.
• Promote clarity and continuity by contributing to solutioning playbooks, decision logs, and architectural documentation.

• A degree in Math, Physics, Statistics, Economics, Computer Science, or a similar domain
• 2+ years of experience in data analytics, consulting, or related role
• Experience working with funnel optimization, user segmentation, cohort analyses, time series analyses, regression models, etc
• Expertise of SQL queries, ETL, A/B Testing, and statistical analysis (e.g. hypothesis testing, experimentation, regressions) with statistical packages, such as Matlab, R, SAS or Python
• Proficiency in one or more analytics & visualization tools (e.g. Chartio, Looker, Tableau)
• The insight to take ambiguous problems and solve them in a structured, hypothesis-driven, data-supported way

• You have 10+ years of experience designing and scaling distributed data systems, with deep expertise in NoSQL technologies like Apache Cassandra, DynamoDB, or ScyllaDB.
• You have a strong command of distributed system concepts such as replication, partitioning, tunable consistency, and failure recovery.
• You’ve led data modeling efforts for high-throughput, low-latency workloads and understand the real-world trade-offs involved in NoSQL schema design.
• You are experienced with caching technologies like Redis or Memcached and know how to layer them effectively over storage systems to optimize for performance and cost.
• You have a customer-first mindset, and thrive when working closely with product and platform teams to translate complex requirements into clean, scalable data models.
• You are skilled at communicating complex architecture decisions and building alignment across infrastructure and product engineering organizations.
• You have a track record of mentoring engineers, influencing data architecture at scale, and fostering best practices in reliability, observability, and data access patterns.
• You document decisions, share learnings, and take pride in contributing to reusable playbooks and durable frameworks for others to build upon.
• Bonus: You’ve worked on or contributed to open-source distributed databases.  

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