Backed by a16z and Founders Fund.
About the role
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'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.
What they're looking for
- Available during Summer 2027 for an internship in New York City
- Experience coding in Python (required) or SQL, R, standard data science libraries (NumPy, Scikit-learn, PyTorch, TensorFlow, Keras), and ML Tools & Libraries (NumPy, SpaCy, NLTK, Scikit-learn, TensorFlow, Keras)
- Experimental design and analysis
- Exploratory data analysis
- Expertise in one of these specialties: optimization and mathematical modeling, machine learning fundamentals, or probabilistic and statistical modeling
- Bonus points: Experience in marketplace design, ridesharing, studying two-sided marketplaces, and/or transportation
More about this role
At Lyft, our purpose is to serve and connect. We aim to achieve this by cultivating a work environment where all team members belong and have the opportunity to thrive.
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'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.
We are hiring for a variety of Data Science interns, focusing on the following specialties:
- Optimization: Construct and fit statistical or optimization models that facilitate automated decision making in the app.
- Machine Learning: Design, build, tune, and deploy machine learning models with a special emphasis on feature engineering and deployment.
- Inference: Design and analyze tests in our dynamic marketplace, estimating statistical and ML models to enable better decisions, and developing and evaluating algorithmic policies...
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