Data Science Intern, Algorithms
Data science interns develop optimization, machine learning, or statistical models for Lyft’s marketplace and transportation platform. The role involves exploratory analysis, production modeling, experimentation, and communicating findings while pursuing a master’s or PhD in a relevant field.
About the job
Responsibilities
- Partner with engineers, product managers, and cross-functional teams to frame mathematical and business problems.
- Perform exploratory data analysis.
- Write production modeling code and collaborate with software engineers to implement algorithms.
- Design and run simulated and live-traffic experiments.
- Analyze experimental and observational data.
- Communicate findings through partner-team collaboration and presentations, and facilitate launch decisions.
Requirements
- Currently pursuing a master’s or PhD degree at a Canadian university in mathematical sciences, economics, data engineering, or a related field.
- Expected graduation between December 2027 and June 2028.
- Available for a Summer 2027 internship in Toronto.
- Python programming experience; SQL or R experience is also relevant.
- Experience with experimental design and analysis and exploratory data analysis.
- Expertise in optimization and mathematical modeling, machine learning fundamentals, or probabilistic and statistical modeling.
- Familiarity with standard data science and machine learning libraries, including NumPy, Scikit-learn, PyTorch, TensorFlow, and Keras.
Nice-to-haves
- Experience with marketplace design, ridesharing, two-sided marketplaces, or transportation.
- Experience with SpaCy or NLTK.
Compensation and Benefits
- CAD $45–$48 per hour, equivalent to CAD $93,600–$99,840 annually based on 2,080 hours.
- Mental health benefits.
- Two paid days off and three paid sick days in addition to holidays.
- Subsidized commuter benefits and Lyft ride credits.
Skills
Python, SQL, R, NumPy, scikit-learn, PyTorch, TensorFlow, Keras, Experimental Design, Exploratory Data Analysis, Optimization, Statistical Modeling, Machine Learning
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