Senior Data Scientist at Toast
We are a rapidly growing company that’s revolutionizing the way the restaurant industry does business by pairing technology with an unrivaled commitment to customer success. We help restaurants streamline operations, increase revenue, and deliver amazing guest experiences through our platform that combines restaurant point of sale, guest-facing technology, and award-winning customer support. As a Toaster, you will be challenged to take on meaningful projects that will help shape the future of the company. Join us as we empower the restaurant community to delight guests, do what they love, and thrive.
As a senior data scientist, you will partner with product managers, software engineers and fellow data scientists as 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. The person may manage 1-2 people depending on desire and if they have previous managerial experience.
What you will do:
- Build machine learning and big data algorithms to create and improve Toast products
- Mentor junior data scientists and analysts on a range of topics spanning both modeling techniques and software engineering best practices
- 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?
- 6+ years experience building and deploying progressively complex and scalable machine learning models; or 3+ years experience and a PhD in a quantitative discipline
- A highly quantitative problem solver who loves to dig into different kinds of data and can confidently and successfully communicate their findings to stakeholders in different departments
- Knowledge of statistics and machine learning concepts including experimental design, hypothesis testing, regression, classification, and clustering
- Experience in one of the following: time series forecasting, natural language processing, neural networks, fraud detection, or recommendation engines
- Knowledge of some of the following languages, tools, and frameworks: Python, R, SQL, Spark, Scala, Java, Scikit-learn
- Exposure to software engineering best practices and tools including object-oriented programming, test-driven development, git, shell scripting, and AWS stack