Staff Data Scientist, Machine Learning - Advertising Science

United States - Remote

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Job's description

About the Team

Come help us build the world's most reliable on-demand logistics engine for delivery! You can read more about the types of Data Scientists we are looking for in our blog post Wanted: Data Scientists with Technical Brilliance AND Business Sense.

About the Role

As a Lead Data Scientist and technical lead, you will help build out algorithms to optimize the advertising auctions we run in the consumer DoorDash app for both merchants and household brands wishing to promote their listings. This will include budget pacing, automated bidding, keyword generation, ad placement logic, and much more. You will work with other data scientists, engineers, and product managers to develop and iterate on models to grow our business and provide better service for our customers.

You’re excited about this opportunity because you will…
  • Lead the development and improvement of DoorDash's nascent Ads auction, on one of the fastest-growing ad platforms ever created
  • Build production-grade algorithms and models that improve the experience of millions of Customers and Advertisers
  • Find new ways to drive business impact and solve complex problems across DoorDash Consumer promotion surface areas
  • Apply stratification, variance reduction, and other advanced experiment design techniques to create A/B tests to efficiently measure the impact of your innovations while minimizing risk to the broader system
  • Mentor and uplevel a talented team of Data Scientists, and ML Engineers 
  • You can find out more on our ML blog here
We’re excited about you because…
  • 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
  • You’re an owner — driven, focused, and quick to take ownership of your work
  • Humble — you’re willing to jump in and you’re open to feedback
  • 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!
  • Growth-minded — you’re eager to expand your skill set and excited to carve out your career path in a hyper-growth setting
  • Desire for impact — ready to take on a lot of responsibility and work collaboratively with your team
  • 6+ years of industry experience developing machine learning and optimization models with business impact — more experience preferred
  • 4+ years of experience working in the Advertising science / modeling / machine learning space
  • 1+ years of industry experience serving in a tech lead role
  • M.S., or PhD. in STEM, Operations Research, or other quantitative field
  • Demonstrated familiarity with programming languages e.g. python and machine learning libraries e.g. SciKit Learn, Spark MLLib
  • Experience shipping production-grade ML models and optimization systems, and designing sophisticated experimentation techniques
  • Good understanding of many quantitative disciplines such as economics, auctions, statistics, operations research, deep learning, forecasting, experimentation, and causal inference 
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 $140,000 - $210,000, 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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