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Lead Applied Scientist - Machine Learning (Pricing)

DoorDashRemote; United StatesFull-time$191kNov 22, 2022
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About the Team

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

We are looking for a talented Lead Applied Scientist for our Pricing and Growth Machine Learning team. The team is responsible for building the ML that powers our growth platform as well as the intelligence of our systems ranging from smart user notifications, effective promotions and optimal solutions. This enables DoorDash’s pricing strategy, efficient marketing prospects, and new user recommendations for our three-sided marketplace of consumers, merchants, and dashers. 

About the Role

As a Lead Applied Scientist you will have the opportunity to define effective approaches to realize DoorDash’s pricing strategy. You will work with other data scientists, engineers, analysts, and product managers to develop and iterate on models to help us grow our business.You will leverage our robust data and infrastructure to develop models that impact millions of users across our three audiences. 

You’re excited about this opportunity because you will…

  • Use causal inference techniques to find effective approach to pricing in sub-marketplaces
  • Experience of shipping production-grade optimization models
  • Experience of developing and running large scale simulations for model prototyping and validation
  • Deep familiarity with complex systems such as Marketplaces, and 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, elasticity curves
  • Good understanding of many quantitative disciplines such as statistics, machine learning, operations research, and causal inference

We’re excited about you because…

  • 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

Experience

  • 5+ years of industry experience developing inference and optimization models with business impact — more experience preferred
  • M.S., or PhD. in Statistics, Computer Science, Electric Engineering, Math, Operations Research, Physics, Economics, or other quantitative field
  • Prior experience with query understanding a plus
  • Deep understanding of at least one of probability, statistics, machine learning, causal inference, prediction, forecasting, optimization
  • Demonstrated coding and programming abilities (Python preferred)

About DoorDash

At DoorDash, our mission to empower local economies shapes how our team members move quickly, learn, and reiterate in order to make impactful decisions that display empathy for our range of users—from Dashers to merchant partners to consumers. We are a technology and logistics company that started with door-to-door delivery, and we are looking for team members who can help us go from a company that is known for delivering food to a company that people turn to for any and all goods.

DoorDash is growing rapidly and changing constantly, which gives our team members the opportunity to share their unique perspectives, solve new challenges, and own their careers. We're committed to supporting employees’ happiness, healthiness, and overall well-being by providing comprehensive benefits and perks including premium healthcare, wellness expense reimbursement, paid parental leave and more.

Our Commitment to Diversity and Inclusion

We’re committed to growing and empowering a more inclusive community within our company, industry, and cities. That’s why we hire and cultivate diverse teams of people from all backgrounds, experiences, and perspectives. We believe that true innovation happens when everyone has room at the table and the tools, resources, and opportunity to excel.

Statement of Non-Discrimination: In keeping with our beliefs and goals, no employee or applicant will face discrimination or harassment based on: race, color, ancestry, national origin, religion, age, gender, marital/domestic partner status, sexual orientation, gender identity or expression, disability status, or veteran status. Above and beyond discrimination and harassment based on “protected categories,” we also strive to prevent other subtler forms of inappropriate behavior (i.e., stereotyping) from ever gaining a foothold in our office. Whether blatant or hidden, barriers to success have no place at DoorDash. We value a diverse workforce – people who identify as women, non-binary or gender non-conforming, LGBTQIA+, American Indian or Native Alaskan, Black or African American, Hispanic or Latinx, Native Hawaiian or Other Pacific Islander, differently-abled, caretakers and parents, and veterans are strongly encouraged to apply. Thank you to the Level Playing Field Institute for this statement of non-discrimination.

Pursuant to the San Francisco Fair Chance Ordinance, Los Angeles Fair Chance Initiative for Hiring Ordinance, and any other state or local hiring regulations, we will consider for employment any qualified applicant, including those with arrest and conviction records, in a manner consistent with the applicable regulation.

Pursuant to the Colorado Fair Pay Act, the base salary range in Colorado for this position is $141,525$191,475, plus opportunities for equity and commission. Compensation in other geographies may vary. 

If you need any accommodations, please inform your recruiting contact upon initial connection.

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