Data Science Intern: Optimize, Model, Deploy

Lyft; New York (NY); Hybrid; USD 80,000 - 85,000; Full time

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

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.