Toast is a rapidly growing startup building the first all-in-one restaurant management software platform. Toasts Android tablet based system helps restaurants operate more efficiently and connect with their customer base in new and innovative ways. Were growing fast and have a customer base comprised of cafes, restaurants, bars and nightclubs across the country. We work hard and care about our customers success and we have a lot of fun doing it. As a startup, we move fast and have a lot of opportunity for career growth, so if youre passionate about your work and want to be in a fun and growing industry, join us! You will be helping Toast to grow our business across the US and internationally.
As a data scientist, you will partner across the business and be a critical contributor to the development of machine learning algorithms using our huge reservoir of data. You will help build product offerings across the Toast platform including Toast Capital, Toast Takeout, and our customer-facing reports.
What you will do:
- Build machine learning and big data algorithms to create and improve Toast products
- Build proprietary big data models to drive business value for both Toast and our restaurant customers
- Collaborate closely with fellow data scientists, product, engineering, and others within the organization to build our restaurant technology platform
- Work effectively in a dynamic, changing environment while focusing on key goals and objectives
Do you have the right ingredients?
- 2+ years experience building and deploying progressively complex and scalable machine learning models
- A highly quantitative problem solver who loves to dig into different kinds of data and can communicate their findings to stakeholders in different departments
- Knowledge of some of the following languages, tools, and frameworks: Python, R, SQL, Spark, Scala, Java, Scikit-learn
- Knowledge of statistics and machine learning concepts including experimental design, hypothesis testing, regression, classification, and clustering
- Exposure to software engineering best practices and tools including object-oriented programming, test-driven development, git, shell scripting, and AWS stack