Impress employers and recruiters.
Choose from hundreds of resume examples.
Impress employers and recruiters.
Choose from hundreds of resume examples.
Tailor your resume to this Data Science Intern, Algorithms (Summer 2027) role.
Rezi rewrites your resume against Lyft's job description. Free.
Tailor your resume to this Data Science Intern, Algorithms (Summer 2027) role.
Rezi rewrites your resume against Lyft's job description. Free.
Don't guess if your resume is good enough.
See how it scores against the Data Science Intern, Algorithms (Summer 2027) posting at Lyft — free, in seconds.
Don't guess if your resume is good enough.
See how it scores against the Data Science Intern, Algorithms (Summer 2027) posting at Lyft — free, in seconds.
About the Role
Lyft’s Data Science Team builds mathematical models underpinning the platform’s core services. We are looking for Masters or PhD students passionate about solving mathematical problems with data and excited about working in a fast-paced, innovative, and collegial environment.
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
Requirements
- 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
- Available during Summer 2027 for an internship
- Experience coding in Python or SQL, R
- Experience with standard data science libraries (NumPy, Scikit-learn, PyTorch, TensorFlow, Keras)
- Experience with ML Tools & Libraries (NumPy, SpaCy, NLTK, Scikit-learn, TensorFlow, Keras)
- Experimental design and analysis experience
- Exploratory data analysis experience
- Expertise in one of these specialties: optimization and mathematical modeling, machine learning fundamentals, or probabilistic and statistical modeling
- Experience in marketplace design, ridesharing, studying two-sided marketplaces, and/or transportation (bonus points)
Skills
- Python
- SQL
- R
- NumPy
- Scikit-learn
- PyTorch
- TensorFlow
- Keras
- SpaCy
- NLTK
- Optimization
- Mathematical modeling
- Machine learning fundamentals
- Probabilistic modeling
- Statistical modeling
- Experimental design
- Experimental analysis
- Exploratory data analysis
Location
- San Francisco
Work Type
- Hybrid
- In-office (3 days per week)
Experience Level
- Masters or PhD student
Education Level
- Masters or PhD
Salary/Compensations
- $58-$62/hour USD
Benefits
- Great medical, dental, and vision insurance options
- Mental health benefits
- 2 days paid time off
- 3 days sick time off
- 401(k) plan
- Pre-tax commuter benefits
- Lyft Pink - Lyft team members get an exclusive opportunity to test new benefits of our Ridership Program
About the Company
- Lyft's purpose is to serve and connect.
- Lyft’s Data Science Team builds mathematical models underpinning the platform’s core services.
- The set of problems that we tackle is incredibly diverse. They cut across optimization, prediction, modeling, inference, transportation, and mapping.
Equal Opportunity
- 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.