About the position
The Experimentation Platform team develops an industry-leading platform that enables data scientists, ML engineers and non-technical users to design, run and analyze experiments and conduct exploratory and causal analysis. At DoorDash, where we run thousands of experiments per month, our mission is to equip all decision makers with rigorous, data-driven insights by democratizing experimentation with quality and velocity. The team consists of a mix of experienced veterans of backend, web, statistical and data infra engineers that works closely with the data science community. Some of the interesting work done in the team was published in articles such as: (1) Meet Dash-AB-The Statistics Engine of Experimentation at DoorDash, (2) Supporting Rapid Product Iteration with an Experimentation Analysis Platform, (3) Experiment Faster and with Less Effort, (4) Interleaving designs, (5) Fractional Factorial Design for Business Policy. We help enable and unlock interesting solutions our product teams use such as (3) The 4 Principles DoorDash Used to Increase Its Logistics Experiment Capacity by 1000%, (4) Improving Online Experiment Capacity by 4X with Parallelization and Increased Sensitivity, etc.
Responsibilities
- Build Experimentation platform that can evolve to handle new statistical methodologies, machine learning and artificial intelligence technologies and advanced causal inference and data mining techniques
- Drive the statistical and ML development of internal platforms, including both the theoretical and engineering aspects, products including A/B testing platform, Causal Inference platform and Adaptive Learning platform (RL/MAB)
- Expand the statistical and causal inference algorithms to support large-scale experimentation volume and computation load and high noise-to-signal business environment
- Apply semi-supervised learning, LLM, active learning, documentation embedding/retrieval and data augmentation strategies to advance the hypothesis generation of the experimentation platform
- Advise data scientists, operators, and engineers across the company on experimental design and adoption of experimentation tools
Requirements
- 10+ years of industry experience of developing statistical or ML models with business impacts
- M.S., or PhD. in Statistics, Causal Inference, Experimentation, Computer Science, Applied Mathematics or other related quantitative fields
- Demonstrated expertise with programming languages, e.g. Python, Java, Kotlin, Go, SciKit Learn, Spark MLLib, etc.
- Experience building reliable, scalable, highly available distributed systems
- You are located or are planning to relocate to San Francisco CA, Sunnyvale, CA, or Seattle, WA
Nice-to-haves
- Deep expertise in mathematics, statistics, causal inference or econometrics
- Fullstack industry experience (web + backend)
- Experience with any of the 'Big Data' technologies (e.g. Postgres, Redis, Elasticsearch, Snowflake, Mode, Segment, Spark etc.)
Benefits
- 401(k) plan with an employer match
- Paid time off
- Paid parental leave
- Wellness benefits
- Several paid holidays
- Paid sick leave in compliance with applicable laws
- Medical, dental, and vision benefits
- Disability and basic life insurance
- Family-forming assistance
- Commuter benefit match
- Mental health program
