Data Scientist II

Summary

Toast builds a technology platform that helps restaurants streamline operations, increase revenue, and deliver exceptional guest experiences. The Data Scientist II will apply statistical analysis, data mining, and machine learning to develop actionable insights and models, while partnering with engineers, product managers, and business teams to drive product and business growth.

Responsibilities

• Apply a diverse set of expertise including data mining, statistical analysis and machine learning to deliver impactful, objective, and actionable data insights that enable informed business and product decisions
• Collaborate with cross-functional teams, including sales, marketing, and product, to identify business opportunities and develop data-driven solutions that drive growth and engagement
• Partner with line of business teams and collaborate with product managers, engineers and data scientists to foster data-driven decisions that yield significant impacts
• Able to effectively communicate analysis, insights and recommendations to high-level business partners in verbal, visual and written formats
• Thrive in a dynamic and rapidly evolving environment

Skills

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

Qualifications

Must Haves

• Bachelors in computer science, engineering, math, statistics, economics, or other quantitative discipline; Masters preferred
• 2+ years of data science experience in an industry environment
• Have solid statistical and machine learning foundations
• Familiar with machine learning concepts (e.g. regression/classification, clustering, offline/online model evaluation)
• Experience with advanced machine learning techniques, including supervised and unsupervised learning, graph algorithms, deep learning (e.g., NLP), recommendation systems, and generative AI
• Experience with Python and SQL, and ML frameworks (e.g. scikit-learn, Tensorflow, PyTorch)
• Experience with cloud solutions, preferably with AWS tooling (e.g. SageMaker, DynamoDB, Athena, Glue, etc.)
• Experience with model workflow orchestration tool (e.g. Airflow)
• Experience collaborating with engineers, product managers, and other cross-functional teams
• Excellent verbal and written communication skills
• Ability to communicate sophisticated quantitative analysis in a clear, precise, and actionable manner

Nice to Haves

• Experience working on LLM applications, including prompting, RAG, and evaluation
• Experience in software engineering best practices and tools including object-oriented programming, test-driven development, CI/CD, git, shell scripting, task orchestration
• Experience shipping machine learning systems in production environments
• Experience in A/B testing and other experimentation methodologies for effective product launch measurement

Benefits

• Competitive compensation and benefits programs
• The means to a healthy lifestyle with the flexibility to meet Toasters’ changing needs
• Cash compensation (overtime, bonus/commissions if eligible)
• Equity
• Benefits
• AI tools provided across all disciplines, from Engineering and Product to Sales and Support
• We embrace a hybrid work model that fosters in-person collaboration while valuing individual needs.