Staff Data Scientist, Toast

• 10+ years of experience in data science with a proven track record of delivering production ML systems that drive measurable impact
• Deep focus on mentorship, having leveled up more junior data scientist
• You’ve been a technical leader for core data science at Toast
• Deep knowledge of statistical modeling, machine learning (e.g., tree-based models, time series, deep learning), and model evaluation
• Experience working with real-world product data at scale and translating ambiguous problems into well-scoped ML solutions
• Experience with distributed data processing and training, real-time inference, and ML Ops frameworks
• Prior experience mentoring other data scientists or acting as a tech lead
• Experience leading experimentation (e.g., A/B testing), causal inference, and real-time decision systems
• Proficiency in Python and SQL, and experience with ML frameworks (e.g., scikit-learn, PyTorch, TensorFlow)
• Strong grasp of software engineering principles including modular design, version control, testing, and CI/CD
• Hands-on experience with cloud platforms (preferably AWS), including tools like SageMaker, Athena, Glue, DynamoDB, and Bedrock
• Excellent communication skills and the ability to influence both technical and non-technical stakeholders
• Strong business acumen with the ability to align technical solutions with company goals

Desirable

• As a Staff Data Scientist, you’ll lead the design and development of scalable ML systems for use cases such as menu recommendation, demand forecasting, offer targeting, and guest personalization.
• You will serve as a technical thought partner across teams, set best practices, and influence the roadmap for ML-driven products that support key business outcomes. Your work will directly shape strategic decisions and enhance customer experience at scale
• Own the full machine learning lifecycle—from problem framing and data exploration to modeling, deployment, and monitoring—for mission-critical initiatives
• Design and implement advanced ML and statistical models that improve product performance, operational efficiency, or customer insights
• Collaborate with engineers, product managers, and business stakeholders to define project scope, success metrics, and integration strategy
• Guide architectural decisions, set modeling standards, and champion best practices for experimentation, validation, and productionization
• Mentor other data scientists and raise the technical bar through design reviews, feedback, and sharing domain expertise
• Proactively identify areas where data science can create business value and lead cross-functional efforts to drive those opportunities forward