About the Role
Lead the end-to-end machine learning lifecycle for critical projects, designing advanced models to enhance product performance and efficiency, collaborating with cross-functional teams, mentoring peers, and driving innovation using cutting-edge AI tools.
Requirements
- Minimum of seven years in data science with demonstrated success in deploying production machine learning systems that yield tangible results
- Expertise in statistical modeling, machine learning techniques including tree-based models, time series analysis, deep learning, and rigorous model evaluation
- Proven ability to handle large-scale product data and convert vague challenges into clearly defined machine learning projects
- Hands-on experience with distributed data processing, training, real-time inference, and machine learning operations frameworks
- Previous roles involving mentorship of data scientists or serving as a technical lead
- Leadership in experimentation methods such as A/B testing, causal inference, and real-time decision systems
- High proficiency in Python and SQL, along with practical knowledge of machine learning libraries like scikit-learn, PyTorch, and TensorFlow
- Solid understanding of software engineering practices including modular design, version control, testing, and continuous integration and deployment
- Practical experience with cloud services, ideally AWS, utilizing tools such as SageMaker, Athena, Glue, DynamoDB, and Bedrock
- Outstanding communication abilities to effectively engage and persuade both technical and non-technical audiences
- Strong business insight to ensure technical strategies support organizational objectives
- Experience developing services using large language models in high-volume production settings
Nice to Have
- Graduate degree in Computer Science, Statistics, or another STEM discipline
- Knowledge of MLOps tools for monitoring, detecting data drift, model retraining, and ensuring explainability
- Experience in fine-tuning large language models and applying reinforcement learning from human feedback to enhance model performance and alignment
Work Arrangement
Hybrid
A day in the life
Manage the complete machine learning process from problem identification and data analysis to model deployment and ongoing monitoring for key projects. Develop sophisticated machine learning and statistical models to boost product effectiveness, operational efficiency, or customer understanding. Work with engineers, product managers, and business partners to outline project parameters, define success indicators, and plan integration. Influence architecture choices, establish modeling guidelines, and promote best practices in experimentation, validation, and production deployment. Provide guidance to fellow data scientists and elevate team capabilities through design critiques, constructive feedback, and knowledge sharing. Identify new opportunities for data science to add business value and lead interdisciplinary initiatives to pursue these prospects. Utilize advanced AI technologies to streamline development, increase productivity, and explore innovative methods, fostering a culture of creativity and efficiency.
What you'll need to thrive
Seven or more years in data science with a history of implementing production machine learning systems that deliver measurable outcomes. In-depth understanding of statistical modeling, machine learning approaches such as tree-based models, time series, deep learning, and model assessment. Experience managing extensive real-world product data and converting unclear issues into precise machine learning solutions. Familiarity with distributed data handling, training, real-time inference, and machine learning operations frameworks. Background in mentoring data scientists or leading technically. Leadership in experimentation like A/B testing, causal analysis, and real-time decision systems. Proficiency in Python, SQL, and machine learning frameworks including scikit-learn, PyTorch, and TensorFlow. Strong command of software engineering concepts like modular architecture, version control, testing, and CI/CD. Hands-on use of cloud platforms, preferably AWS, with tools like SageMaker, Athena, Glue, DynamoDB, and Bedrock. Excellent communication skills to effectively engage and influence diverse stakeholders. Sharp business sense to align technical approaches with corporate goals. Experience creating services based on large language models in scalable production environments.
Bonus ingredients
Preference for an advanced degree in Computer Science, Statistics, or a related STEM field. Awareness of MLOps tools for monitoring, detecting drift, retraining models, and ensuring transparency. Experience in refining large language models and applying reinforcement learning from human feedback to boost model performance and alignment.
AI at Toast
The company values continuous learning and building, believing that mastering new AI tools enables faster, more independent, and higher-quality work for customers. These tools are available across all departments, from Engineering and Product to Sales and Support, and employees are already generating significant value with them. Successful candidates embrace changes that enhance customer-focused innovation, which is central to the organizational culture.
Our Total Rewards Philosophy
The compensation and benefits programs are designed to attract, retain, and motivate top talent in the industry. The total rewards package includes competitive earnings and supports a healthy lifestyle with flexibility to adapt to employees' evolving needs. More details are available at the provided benefits link.
How Toast Uses AI in its Hiring Process
AI tools assist recruiters and interviewers with tasks like note-taking, summarization, and interview documentation to allow them to concentrate on conversations with candidates. All hiring decisions are made by people. Additional information is accessible via the provided link.
Our Approach to Hybrid Working
A hybrid work model encourages in-person collaboration while accommodating individual preferences, aiming to build a connected culture that supports the hospitality community regardless of location. Regional office expectations can be found on the locations page.
Diversity, Equity, and Inclusion is Baked into our Recipe for Success
Employee well-being is crucial to success, and the diversity of the restaurant industry is embraced with authenticity, inclusivity, respect, and humility. These principles are integrated into culture and design to create fair opportunities and enhance service delivery.
We Thrive Together
The hybrid work model promotes collaborative in-person work while respecting individual requirements, striving to foster a connected culture that empowers the restaurant community. Global and regional work details are available on the locations page.
