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Senior Data Scientist
Support, NLP and Causal Inference
Posted on 4/7/2022
INACTIVE
Locations
San Francisco, CA, USA • Remote • United States
Experience Level
Entry
Junior
Mid
Senior
Expert
Desired Skills
Apache Spark
Data Structures & Algorithms
Research
Natural Language Processing (NLP)
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 - 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
  • 4+ years of industry experience developing optimization 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, Math, Operations Research, Physics, Economics, or other quantitative field
  • Deep understanding of natural language processing techniques and procedures for efficiently acquiring and validating human-labeled data
  • Good understanding of many quantitative disciplines such as statistics, machine learning, operations research, and causal inference
  • 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 models
  • Familiarity with advanced causal inferences techniques and contextual bandit algorithms preferred
Responsibilities
  • Lead the development of DoorDash's support chatbot: Applying active learning, semi-supervised learning, weak label generation, and data augmentation strategies to improve the consumer, dasher, and merchant support experience
  • Drive the personalization of DoorDash's credit and refunds policies: Using uplift/heterogeneous treatment effect models and contextual bandits to improve consumer retention after negative delivery experiences
  • Spearhead the creation of next generation agent tools: Building contextual bandits to recommend replies to support agents and generative models for agent text auto-completion to improve the consumer, dasher, and merchant experience while reducing agent effort
  • 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
  • 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.