Data Science Engineer Intern, Caviar Logistics Platform

  • San Francisco, CA, USA
  • Intern

Company Description

Caviar is changing the way businesses and consumers order food from restaurants. We believe that everyone should have access to the best eateries in their city without any hassles. Want your favorite burger joint, but hate the traffic and long lines? We're a team of passionate foodies solving that exact problem for your home and office. At Caviar, we believe in an environment that allows you to be creative and go beyond the call of duty. We're looking for exceptionally talented individuals who want to learn and grow with the company. Each and every day counts - you'll make a direct impact to the business starting from Day 1.

Caviar’s mission is to deliver a delightful experience for couriers, restaurants and diners. To do this, we are building a reliable and scalable on-demand logistics platform powered by machine learning. The logistics platform is responsible for actually moving food from the restaurant to the diner while ensuring a smooth delivery and a delightful experience. This is a challenging problem that requires not only deep technical expertise but cross-team collaboration across multiple functions and a desire to make a real-world business impact.

Job Description

For this 12 week summer internship, you will:

  • Solve business problems that are massively impactful to Caviar’s revenue and profitability

  • Conduct data analysis to identify areas of opportunity to improve logistical efficiency

  • Build machine learning models that make critical decisions in an automated manner

  • Write production code to deploy machine learning models

  • Run experiments to improve logistical efficiency and report results to leadership

  • Help define the future of Caviar’s logistics platform including everything from technical infrastructure to the specifics of the ML models

  • Some examples of problems you may work on: optimizing the assignment of couriers to orders, building models and algorithms for pricing and courier payout, predicting restaurant’s food preparation time.

Qualifications

You have:

  • Pursuing a degree in Computer Science, Machine Learning, Statistics, Physical Sciences, Economics, or a related technical field

  • Familiarity with Linux/OS X command line, version control software (git), and general software development

  • Experience performing data analysis using Python (Jupyter, pandas etc.) and SQL

  • Understanding of machine learning and statistics

Even better:

  • Pursuing a graduate degree (M.S., PhD.) in Computer Science, Machine Learning, Statistics, Physical Sciences, Economics, or a related technical field

  • Familiarity with Python machine learning libraries (scikit-learn)

  • Experience productionizing machine learning models to solve complex business problems

Technologies we use and teach:

  • Python, Jupyter

  • Machine Learning with scikit-learn

  • Ruby on Rails

Additional Information

At Square, our purpose is to empower – within and outside of our walls. In order to build the best tools for the businesses and customers we support all over the world, we have to start at home with a workforce as diverse and empowered as our sellers. To this end, we take great care to evaluate all employees and job applicants equally, based on merit, competence, and qualifications. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, disability status, or any other characteristic protected by law. We encourage candidates from all backgrounds to apply and always consider qualified applicants with arrest and conviction records, in accordance with the San Francisco Fair Chance Ordinance.

Applicants in need of special assistance or accommodation during the interview process or in accessing our website may contact us. We will treat your request as confidentially as possible. In your email, please include your name and preferred method of contact, and we will respond as soon as possible.