Data Science Tech Lead, Machine Learning

Key Skills

Machine learning techniques, Optimization, Probability, Python Programming, PyTorch, Scikit-learn, SQL, Statistics, TensorFlow

Job Description

About the Team

Come help us build the world's most reliable on-demand, logistics engine for delivery!

We are looking for a Tech Lead for our Growth Machine Learning team. The team is responsible for building the ML that powers our growth platform, responsible for intelligence of our systems ranging from smart user notifications, efficient marketing prospects, and new user recommendations for DoorDash's three-sided marketplace of consumers, merchants, and dashers. 

About the Role

You will be part of a team of world-class engineers in Machine Learning science, redefining the new user acquisition and discovery experience through cutting edge approaches, and defining effective approaches to realize DoorDash’s pricing strategy. You will be a tech lead on the team that ensures we provide a consistent, personalized experience to our users, no matter where they sit on their journey with us, and help make critical optimization algorithms that drive effective promotions and targeting across our various customer bases. You will leverage our robust data and infrastructure to develop models that impact millions of users across our three audiences. You will partner cross functionally to set the strategies that help us grow our business and lead efforts to execute those strategies.

You’re excited about this opportunity because you will…

• 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. 

We’re excited about you because…

• 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
• You must be located near one of our engineering hubs which includes: San Francisco, Sunnyvale, Los Angeles, Seattle, and New York

Compensation

The location-specific base salary range for this position is listed below. Compensation in other geographies may vary.

Actual compensation within the pay range will be decided based on factors including, but not limited to, skills, prior relevant experience, and specific work location. For roles that are available to be filled remotely, base salary is localized according to employee work location. Please discuss your intended work location with your recruiter for more information.

DoorDash cares about you and your overall well-being, and that’s why we offer a comprehensive benefits package, for full-time employees, that includes healthcare benefits, a 401(k) plan including an employer match, short-term and long-term disability coverage, basic life insurance, wellbeing benefits, paid time off, paid parental leave, and several paid holidays, among others.

In addition to base salary, the compensation package for this role also includes opportunities for equity grants.

• California Pay Range: $208,000—$282,000 USD
• New York Pay Range: $208,000—$282,000 USD
• Washington Pay Range: $208,000—$282,000 USD