Data Science Intern, Algorithms (Summer 2027)

Lyft · Toronto, Canadajunior

In short

▸Trabajarás en modelos matemáticos y algoritmos para optimizar decisiones en la plataforma de transporte.
▸Tu trabajo diario incluirá análisis de datos, diseño de experimentos y colaboración con ingenieros para llevar modelos a producción.
▸Destaca que el equipo abarca desde optimización hasta inferencia estadística en un entorno de mercado dinámico.

No se requiere inglés, pero la comunicación con equipos multidisciplinarios en inglés será

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What they ask for

✓Estudiante de maestría o doctorado en ciencias matemáticas, ciencias de datos, ingeniería o economía con graduación entre diciembre 2027 y j
✓Disponibilidad para un internado presencial en Toronto durante el verano de 2027.
✓Experiencia en programación con Python, SQL o R, y en bibliotecas comunes como NumPy, Scikit-learn.
✓Habilidades en análisis exploratorio de datos y diseño de experimentos.
✓Conocimiento en una de estas áreas: optimización, machine learning o modelado estadístico.
✓Experiencia previa en mercados digitales (valor agregado, no obligatorio).

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PythonSQLRNumPyScikit-learnPyTorchTensorFlowKerasSpaCyNLTK

Who should you write to at Lyft?

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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

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