Data Science Tech Lead, DoorDash
Machine Learning
$208-282k
Local food delivery platform
Job no longer available
Local food delivery platform
1001+ employees
Job no longer available
$208-282k
1001+ employees
Company mission
To empower local economies by connecting food lovers with great local restaurants
Role
Who you are
- 5+ years of industry experience developing inference and optimization models with business impact — more experience preferred
- 2+ years of experience in a technical management role
- M.S., or PhD. in Statistics, Computer Science, Electric Engineering, Math, Operations Research, Physics, Economics, or other quantitative field
- Deep understanding of at least one of probability, statistics, machine learning, causal inference, prediction, forecasting, optimization
- Demonstrated familiarity with programming languages e.g. python and machine learning libraries e.g. SciKit Learn, PyTorch/TensorFlow, SQL
- Desire for impact — ready to take on a lot of responsibility and work collaboratively with your team
- Growth-minded — you’re eager to expand your skill set and excited to carve out your career path in a hyper-growth setting
- Adaptable, resilient, and able to thrive in ambiguity — things change quickly in our fast-paced startup and you’ll need to be able to keep up!
- Humble — you’re willing to jump in and you’re open to feedback
- You’re an owner — driven, focused, and quick to take ownership of your work
- High-energy and confident — you keep the mission in mind, take ideas and help them grow using data and rigorous testing, show evidence of progress and then double down
- Good understanding of many quantitative disciplines such as statistics, machine learning, operations research, and causal inference
What the job involves
- Lead the effort for growth machine learning: Applying supervised, semi-supervised, active-learning, embedding, and causal inference approaches to improve customer acquisition, retention, resurrection, and development
- Work with the team to build models to optimize promotions, non-monetary interventions, and personalized recommendations across different segments of customers
- Lead and implement end to end ML products, and ship both real time and batch production models via experimentation
- Use causal inference techniques to find effective approaches to recommend across various segments of customers
- Ship production-grade optimization models
- Exercise variety of techniques to optimize complex systems such as Marketplaces, with domain deep domain knowledge in OR (stochastic optimization, convex optimization, dynamic programming, MIPs, sequential decision models), applied experience with Machine Learning (DL/ NN, Tree Based models,etc.), contextual bandits and reinforcement learning problems
- Collaborate with the team on a wide spectrum of ML techniques
- You can find out more on our ML blog post here
Our take
DoorDash was an early entrant into the last-mile restaurant delivery business, which is now crowded with the likes of Uber Eats, Postmates and GrubHub. They offer restaurants an end-to-end delivery platform, generating new business and access to a network of delivery drivers.
To differentiate itself in the market, DoorDash focused on areas with fewer competitors and found success in this untapped market. Other factors, such as the company's targeting of restaurants which many not typically offer a delivery service, also contribute to its profitability. The company made its stock market debut in 2020 with one of the biggest IPOs of the year.
However, in light of increasing discontent around DoorDash's commission structure from restaurants, competition remains fierce. The business recently responded by publishing a transparent fee structure, but the number of options open to restaurants means DoorDash must fight to keep its customers loyal. Despite this, by focusing on consumer retention, logistics and technology, the company is likely able to maintain its existing growth and revenue.
Freddie
Company Specialist at Welcome to the Jungle
Insights
13% employee growth in 12 months
Company
Funding (last 2 of 10 rounds)
Jun 2020
$400m
SERIES H
Nov 2019
$100m
SERIES G
Total funding: $2.5bn
Company benefits
- Company stock options
- Work from home stipend
- Unlimited paid time off policy
- Work from home opportunities
- Health insurance
Company HQ
Mid-Market, San Francisco, CA
Leadership
Tony Xu
(CEO)Tony studied at UCB and worked as a Matrix Partners Associate while studying for an MBA at Stanford. He combined this experience to co-found DoorDash in January 2013, and has served as CEO since June 2013.
Stanley Tang
(CPO)Previously studied Computer Science at Stanford, before working at Facebook as a Software Engineer.
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