Data Science Intern, Algorithms (Summer 2027)

Socket.dev; San Francisco (CA); Hybrid; USD 80,000 - 85,000; Full time

Benefits offered by this job

Medical insurance

Mental health benefits

PTO 2 days

Sick time 3 days

401(k)

Pre-tax commuter benefits

Lyft Pink

Job summary

Lyft is hiring for a Data Science internship in San Francisco. Masters or PhD students will work on optimization, machine learning, and inference across the marketplace, collaborating with engineers and product teams.

Ideal candidates are pursuing advanced degrees with a graduation date between December 2027 and June 2028, and are available Summer 2027 in San Francisco. The role is hybrid with three in-office days per week and production modeling responsibilities.

Qualifications

• Pursuing a Masters or PhD in a mathematical or data-related field with graduation between Dec 2027 and Jun 2028
• Available Summer 2027 for an internship in San Francisco
• Experience coding in Python (required) or SQL/R and familiarity with ML libraries

Responsibilities

• Frame problems mathematically and in business context with cross-functional partners
• Perform exploratory data analysis to understand problems
• 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 inform launches

Skills

Python

SQL

R

Education

Masters or PhD student

Tools

NumPy

Scikit-learn

TensorFlow

PyTorch

Keras

SpaCy

NLTK

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 San Francisco
• 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 San Francisco 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.