Data Science Intern, Algorithms (Summer 2027 - SF/NYC)

Lyft

New York (NY)

Hybrid

USD 80,000 - 85,000

Full time

14 days+
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Benefits offered by this job

Medical insurance
Mental health benefits
Paid time off
Sick leave

Job summary

Lyft invites Masters or PhD students to join our Data Science internship program in New York City for Summer 2027. Candidates will tackle optimization, ML, and inference challenges across our marketplace and ridesharing platform.

You will work with engineers and product teams, perform exploratory analysis, and help deploy models in production while gaining hands-on experience in a fast-paced environment.

Qualifications

  • Pursuing Masters or PhD in mathematical sciences or related field with graduation between 2027-2028.
  • Available Summer 2027 for an internship in New York City.
  • Proficient in Python; knowledge of SQL or R; familiarity with standard data science libraries.
  • Experience in experimental design and data analysis is required.
  • Expertise in optimization, ML, or probabilistic/statistical modeling is preferred.
  • Bonus for marketplace design, ridesharing, or two-sided marketplaces experience.

Responsibilities

  • Partner with Engineers, PMs, and cross-functional teams to frame problems mathematically and in business terms.
  • Perform exploratory data analysis to understand problems deeply.
  • Write production modeling code and collaborate with software engineers to deploy algorithms.
  • Design and run simulated and live traffic experiments.
  • Analyze data and communicate findings to partner teams to aid launches.

Skills

Python
SQL
R

Education

Masters or PhD in a relevant field

Tools

NumPy
Scikit-learn
PyTorch
TensorFlow
Keras

Job description

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 in our pricing, dispatch, and incentives systems.

You will report into a Science Manager.

Responsibilities:
  • Partner with Engineers, Product Managers, and other cross-functional partners to frame problems, both mathematically and within the business context
  • Perform exploratory data analysis to gain a deeper understanding of the problem
  • Write production modeling code; collaborate with software engineers to implement algorithms in production
  • Design and run both simulated and live traffic experiments
  • Analyze experimental and observational data; communicate findings including working with partner teams and presentations; facilitate launch decisions
Experience:
  • Currently pursuing a Masters or PhD degree in mathematical sciences (Operations Research, Computer Science, Statistics, Applied Mathematics, Theoretical Physics, Behavioral Science, Electrical Engineering, etc.), Economics (Microeconomics Theory, Econometrics etc.), Data Engineering; or a related field; with a graduation date between December 2027 and June 2028 (required)
  • 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
Benefits:
  • Great medical, dental, and vision insurance options
  • Mental health benefits
  • In addition to holidays, interns receive 2 days 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 opportunity employer committed to an inclusive workplace that fosters belonging. All qualified applicants will receive consideration for employment without regards to race, color, religion, sex, sexual orientation, gender identity, national origin, disability status, protected veteran status, age, genetic information, or any other basis prohibited by law. We also consider qualified applicants with criminal histories consistent with applicable federal, state and local law.

Lyft highly values having employees working in-office to foster a collaborative work environment and company culture. This role will be in-office on a hybrid schedule — Team Members will be expected to work in the office 3 days per week on Mondays, Wednesdays, and Thursdays. Lyft considers working in the office at least 3 days per week to be an essential function of this hybrid role. #Hybrid

The expected base pay range for this position in the New York City area is $58-$62/hour USD, not inclusive of potential equity offering, bonus or benefits. Salary ranges are dependent on a variety of factors, including qualifications, experience and geographic location.

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