Data Scientist

Join the Toast AI Engineering team as a Data Scientist, where you will embed data science capabilities into the Toast platform. You will collaborate with engineers and product managers to develop statistical and machine learning models that drive key product lines. Your expertise in data mining, statistical analysis, and machine learning will enable you to deliver impactful insights that inform business and product decisions. You will also work with cross-functional teams to identify business opportunities and develop data-driven solutions.

- Collaborate with cross-functional teams to identify business opportunities and develop data-driven solutions. - Apply expertise in data mining, statistical analysis, and machine learning to deliver impactful insights. - Communicate analysis, insights, and recommendations to high-level business partners in a clear and actionable manner.

- Excellent verbal and written communication skills - Experience with Python and SQL, and ML frameworks (e.g. scikit-learn, Tensorflow, PyTorch) - Ability to communicate sophisticated quantitative analysis in a clear, precise, and actionable manner - Have solid statistical and machine learning foundations. Familiar with machine learning concepts (e.g. regression/classification, clustering, offline/online model evaluation) - Experience with cloud solutions, preferably with AWS tooling (e.g. SageMaker, DynamoDB, Athena, Glue, etc.) - Experience with model workflow orchestration tool (e.g. Airflow) - 2+ years of data science experience in an industry environment - Experience with advanced machine learning techniques, including supervised and unsupervised learning, graph algorithms, deep learning (e.g., NLP), recommendation systems, and generative AI - Experience collaborating with engineers, product managers, and other cross-functional teams - Bachelors in computer science, engineering, math, statistics, economics, or other quantitative discipline; Masters preferred - Experience working on LLM applications, including prompting, RAG, and evaluation - Experience shipping machine learning systems in production environments - Experience in software engineering best practices and tools including object-oriented programming, test-driven development, CI/CD, git, shell scripting, task orchestration - Experience in A/B testing and other experimentation methodologies for effective product launch measurement