Data Science Intern, Algorithms (Summer 2027 - Toronto)
Lyft is hiring a Data Science Intern, Algorithms (Summer 2027 - Toronto) (Canada). Level rates it ; you can apply on Lyft.
Level's read on this job
- On Level's scale, this role is Builds AI ●●●●: the job is building AI systems.
- Tools and skills the posting cites include PyTorch, TensorFlow and scikit-learn.
- Level classifies it as an internship.
- Level lists 177 other open roles at Lyft.
AI in this role
Design, build, and deploy machine learning and optimization models for Lyft's core algorithmic services as a Data Science Intern.
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 at a university in Canada (required) 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; AND with a graduation date between December 2027 and June 2028 (required)
- Available during Summer 2027 for an internship in Toronto
- 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:
- Mental health benefits
- In addition to holidays, interns receive 2 days paid time off and 3 days sick time off
- Subsidized commuter benefits and Lyft ride credits
Lyft is committed to creating an inclusive workforce that fosters belonging. Lyft believes that every person has a right to equal employment opportunities without discrimination because of race, ancestry, place of origin, colour, ethnic origin, citizenship, creed, sex, sexual orientation, gender identity, gender expression, age, marital status, family status, disability, pardoned record of offences, or any other basis protected by applicable law or by Company policy. Lyft also strives for a healthy and safe workplace and strictly prohibits harassment of any kind. Accommodation for persons with disabilities will be provided upon request in accordance with applicable law during the application and hiring process. Please contact your recruiter if you wish to make such a request.
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 at least 3 days per week, including 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 Toronto area is CAD $45 - CAD $48 per hour. 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.
Lyft may use artificial intelligence to screen applicants, however, Lyft employees make the ultimate selection and hiring decisions.
This is a new position.
How we rate this
Data Science Intern, Algorithms (Summer 2027 - Toronto) at Lyft rates 85 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Questions you could be asked
- Tell me about a project where machine learning was part of your work. What did you do?
- Tell me about a project where optimization was part of your work. What did you do?
- Tell me about a project where inference was part of your work. What did you do?
- Tell me about a project where feature engineering was part of your work. What did you do?
- Tell me about a project where statistical modeling was part of your work. What did you do?
Adapt your resume
- List these exact terms on your resume: Machine learning, Optimization, Inference, Feature Engineering, and Statistical Modeling. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.
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