Data Science Intern, Algorithms (Summer 2027)

Lyft; New York (NY); Híbrido; USD 80 000 - 85 000; Tempo parcial

Vantagens oferecidas por esta oferta de emprego

Health insurance

Paid time off

401(k) plan

Resumo da oferta

Lyft is seeking Masters/PhD candidates for a Summer 2027 data science internship in New York City. You will join the Data Science Team and work on optimization, ML, and inference problems across the platform.

Responsibilities include problem framing with cross-functional teams, exploratory data analysis, writing production modeling code, and running experiments to inform pricing, dispatch, and incentives decisions. Hybrid in-office role.

Qualificações

• Currently pursuing a Masters or PhD in a related field with expected graduation between Dec 2027 and Jun 2028.
• Available during Summer 2027 for an internship in New York City.
• Proficient in Python; knowledge of SQL or R is a plus.
• Experience with data analysis and experimental design.

Responsabilidades

• Frame problems mathematically with cross-functional partners and business context.
• Perform exploratory data analysis to gain deep understanding of problems.
• Write production modeling code and collaborate with engineers to deploy algorithms.
• Design and run both simulated and live traffic experiments.
• Analyze data, communicate findings, and support launch decisions with partners.

Conhecimentos

Python

SQL

R

NumPy

Scikit-learn

PyTorch

TensorFlow

Keras

Formação académica

Masters/PhD candidate

Ferramentas

NumPy

SpaCy

NLTK

TensorFlow

Keras

PyTorch

Scikit-learn

Descrição da oferta de emprego

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.

Specialties we are hiring for

• 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. Your recruiter can share more information about the various in-office perks Lyft offers.

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. Your recruiter can share more information about the salary range specific to your working location and other factors during the hiring process.