Data Science Intern, Algorithms (Summer 2027)

Résumé du poste

You will partner with cross-functional teams to frame business problems mathematically and develop production-ready modeling code. Additionally, you will design and analyze traffic experiments to facilitate data-driven launch decisions.

Détails du poste

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.

Ce que vous ferez

You will partner with cross-functional teams to frame business problems mathematically and develop production-ready modeling code. Additionally, you will design and analyze traffic experiments to facilitate data-driven launch decisions.

Exigences

Candidates must be currently pursuing a Masters or PhD in a quantitative field at a Canadian university with a graduation date between December 2027 and June 2028. Proficiency in Python and experience with standard data science libraries and experimental design are required.

Avantages

• Mental health benefits • Paid time off • Sick time off • Subsidized commuter benefits • Lyft ride credits

Compétences indiquées

• SQL · Souhaitée
• Apprentissage automatique · Souhaitée
• Python · Souhaitée

Autres compétences pertinentes

Relevées dans la description du poste. Confirmez les exigences importantes ci-dessus.

• Python
• SQL
• R
• NumPy
• Scikit-learn
• PyTorch
• TensorFlow
• Keras
• SpaCy
• NLTK
• Mathematical modeling
• Machine learning
• Statistical modeling
• Experimental design
• Exploratory data analysis
• Optimization
• Theoretical Physics
• Time Off Management
• SpaCy (NLP Software)
• Operations Research
• Artificial Intelligence
• Algorithms
• Electrical Engineering
• Applied Mathematics
• Behavioral Science
• Mental Health
• Decision Making
• Computer Science
• Data Engineering
• Design of Experiments (DOE)
• Disabilities
• Economics
• Econometrics
• Exploratory Data Analysis
• Forecasting
• R (Programming Language)
• Innovation
• Python (Programming Language)
• Machine Learning
• Mathematics
• Mathematical Modeling
• Mathematical Sciences
• Microeconomics
• NLTK (NLP Analysis)
• NumPy (Python Package)
• Presentations
• Software Engineering
• SQL (Programming Language)
• Statistical Modeling
• Statistics

Domaines d’emploi

• Data & Analytics
• Technology
• Science & Research
• Software
• Transportation
• Algorithm Engineer
• Data Scientist
• Systems Analysts
• Data Scientists