Data Science Intern, Decisions - Inference (2021)

San Francisco, CA

Internship
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Posted 3 weeks ago

Lyft’s Data Science Team builds mathematical models underpinning the platform’s core services. Compared to other technology companies of a similar size, the set of problems that we tackle is incredibly diverse. They cut across optimization, prediction, modeling, inference, transportation, and mapping. We are hiring motivated experts in each of these fields. We're looking for Masters or PhD students who are passionate about solving mathematical problems with data, and are excited about working in a fast-paced, innovative and collegial environment.

At Lyft, community is what we are and it’s what we do. It’s what makes us different. To create the best ride for all, we start in our own community by creating an open, inclusive, and diverse organization where all team members are recognized for what they bring.

As a Data Science Intern on the Decisions: Inference track, you will work on designing and analyzing tests in our dynamic marketplace, estimating statistical and ML models to enable better decisions, and developing and evaluating algorithmic policies in our pricing, dispatch, and incentives systems.  We expect candidates to have strong probability skills and statistical rigor, knowledge of causal inference, and familiarity with advanced modeling techniques from fields such as econometrics, statistics, and machine learning.

You will report into a Science Manager.

Responsibilities:
  • Partner with Engineers, Product Managers, and Business Partners to frame problems, both mathematically and within the business context.
  • Perform exploratory data analysis to gain a deeper understanding of the problem
  • Construct and fit statistical and machine learning models
  • Write production modeling code; collaborate with engineers to implement algorithms in production systems
  • Design and conduct marketplace experiments in a setting with spatial interference patterns and time-varying treatment effects
  • Analyze experimental and observational data; communicate findings including working with partner teams and presentations; facilitate launch decisions
  • Prototype modeling approaches that reduce error in estimators, leveraging high-dimensional marketplace activity data
  • Rigorously evaluate and compare policies based on models, using modern approaches such as off-policy evaluation
Experience:
  • Currently pursuing a Masters or PhD degree in Statistics, Economics, Machine Learning, Biostatistics, or Computer Science, or a related field
  • Experience coding in Python or R, SQL, and standard data science libraries (NumPy, Scikit-learn, PyTorch, TensorFlow, Keras)
  • Experimental design and analysis of randomized experiments such as A/B tests
  • Probabilistic and statistical modeling
  • Exploratory data analysis
  • Bonus points: Experience in studying two-sided marketplaces
Benefits:
  • Great medical, dental, and vision insurance options
  • In addition to holidays, interns receive 1 day paid time off and 3 days sick time off
  • 401(k) plan to help save for your future
  • Pre-tax commuter benefits
  • Lyft Pink - Lyft team members get an exclusive opportunity to test new benefits of our Ridership program

Lyft is an Equal Employment Opportunity employer that proudly pursues and hires a diverse workforce. Lyft does not make hiring or employment decisions on the basis of race, color, religion or religious belief, ethnic or national origin, nationality, sex, gender, gender identity, sexual orientation, disability, age, military or veteran status, or any other basis protected by applicable local, state, or federal laws or prohibited by Company policy. Lyft also strives for a healthy and safe workplace and strictly prohibits harassment of any kind. Pursuant to the San Francisco Fair Chance Ordinance and other similar state laws and local ordinances, and its internal policy, Lyft will also consider for employment qualified applicants with arrest and conviction records.

Job tags: Economics Keras Machine Learning ML NumPy Python PyTorch R Scikit-Learn SQL TensorFlow
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