Data Scientist at Toast
We’re not your traditional tech company. We recognize that talented people often come from outside the tech space. No matter what your background is, there’s a place for you at Toast. We’ve 86’d the conventional workplace for one where people can truly bring their full selves to work. Together, we empower restaurants of all sizes to build great teams, increase revenue, improve operations, and delight their guests. We pair our deep understanding of the restaurant industry with powerful cloud based software and restaurant-grade hardware to deliver an intuitive all-in-one platform. Join us on our mission to empower the restaurant community to delight guests, do what they love, and thrive.
Bready* to make a change?
As a data scientist, you will partner across the R&D org and be a critical contributor to the machine learning algorithms using our huge reservoir of menu data. You will help build product offerings across the Toast platform such as a universal menu record and consumer recommendation engine.
About this Roll*:
- 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
- Ability to effectively work with business and data science leads: strong communication skills, ability to synthesize conclusions for non-experts and desire to influence business decisions
Do you have the right ingredients*?
- Bachelor's or Master’s degree; 3+ years of experience in a Data Science, Machine Learning, Software Engineering or similar position
- 2+ years experience building and deploying progressively complex and scalable machine learning models in production environments
- Experience with Python and SQL; knowledge of some of the following languages, tools, and frameworks: R, Spark, Scala, Java, Scikit-learn, etc.
- Exposure to software engineering best practices and tools including object-oriented programming, test-driven development, git, shell scripting, and AWS stack (Sagemaker, Lambda, Glue, etc.)
- Knowledge of statistics and machine learning concepts including experimental design, hypothesis testing, regression, classification, and clustering
- Problem solver who loves to dig into different kinds of data and can communicate their findings to stakeholders in different departments
- Experience with advanced machine learning methods, particularly in natural language processing applications; such as supervised/unsupervised learning, recommendation systems, reinforcement learning, deep learning, etc.