Facebook pixel

Senior Data Scientist
Machine Learning, Search & Recommendation
Confirmed live in the last 24 hours
Locations
Remote • United States
Experience Level
Entry
Junior
Mid
Senior
Expert
Desired Skills
Apache Spark
Data Structures & Algorithms
Research
Python
Operations Research
Scikit-Learn
Requirements
  • 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 as things change quickly in our fast-paced environment!
  • 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
  • 4+ years of industry experience developing recommendation/search models with business impact - more experience preferred
  • 1+ years of industry experience serving in a tech lead role
  • 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
  • Good working knowledge of embedding based methods, deep learning models, and graph based models preferred
  • Demonstrated familiarity with programming languages e.g. python and machine learning libraries e.g. SciKit Learn, Spark MLLib
  • Experience productionizing and A/B testing different machine learning model
Responsibilities
  • Lead the effort for query understanding: Applying active learning, semi-supervised learning, query embedding, product and merchant embedding, entity extraction and canonicalization to improve understanding of the consumer's intent as expressed by search query
  • Drive the personalization for search and recommendation: Building models to understand user's preferences for merchant segments, product categories, dish categories, dietary preferences, etc., furthermore the personalization based on these learned preferences, and addressing the potentially significant cold start problem
  • Extend the search and recommendation platform from the merchant level to the item level: At the item level, the candidate set is billions in size instead of millions at the merchant level. This 1,000x increase in candidate set makes the problem much more challenging and exciting. Additional familiarity with explore/exploit algorithms, and causal inference techniques are a plus
  • You can find out more on our ML blog post here
DoorDash

5,001-10,000 employees

Local food delivery from restaurants
Company mission
DoorDash is working to empower local communities and in turn, creating new ways for people to earn, work, and thrive. The company operates the largest food delivery platform in the United States.
Benefits
  • Health & Wellness - Premium medical, dental, and vision insurance plans, including fertility coverage. Monthly gym and wellness reimbursement.
  • Compensation - Competitive salary with bi-annual performance reviews. Meaningful equity opportunities - with quarterly vesting.
  • Time When You Need It - Flexible vacation days for salaried employees. Generous vacation and sick days for hourly team members. Paid Parental Leave to support our DoorDash families.
  • Flexible Work Support - At-home office equipment and monthly WiFi support while working from home. Enjoy your favorite lunch on us while working in one of our offices.
Company Values
  • We are one team
  • Make room at the table. We’re committed to growing and empowering a more diverse and inclusive community. We believe that true innovation happens when everyone has the tools, resources and opportunity to thrive.
  • Think outside the room. We strive to be as inclusive as possible and consider those who may not be in the room when making decisions.
  • One team, one fight. We’re in this together, and both success and failure are shared. We are intentional about creating a high-accountability, no-blame culture.