Data Science Intern, Algorithms (Summer 2027)

Lyft · San Francisco, CAjunior

In short

▸Internado en Ciencia de Datos enfocado en algoritmos para modelos matemáticos del sistema de Lyft.
▸Trabajas con equipos técnicos y de producto para diseñar, probar y desplegar modelos en entornos reales, usando análisis de datos y experimentos.
▸Destacado: Impacto directo en decisiones clave del mercado de transporte, con exposición a problemas de optimización, predicción y políticas de precios.

Required proficiency in English for collaboration and documentation.

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What they ask for

✓Licenciatura en ciencias matemáticas, ingeniería o campo relacionado con graduación entre diciembre 2027 y junio 2028.
✓Disponibilidad para trabajar en San Francisco durante el verano de 2027.
✓Experiencia comprobada en Python, SQL o R y uso de librerías como NumPy, Scikit-learn, PyTorch, TensorFlow, Keras.
✓Habilidades en análisis exploratorio de datos y diseño de experimentos.
✓Conocimiento sólido en una de: optimización, ML fundamental o modelado estadístico/probabilístico.
✓Preferible experiencia en mercados de doble lado, ride-sharing o transporte.

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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 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 (required) • Available during Summer 2027 for an internship in San Francisco • 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 <

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