Data Scientist II

Role overview A data scientist role embedded within an AI engineering team at a restaurant technology platform. The position partners closely with engineers and product managers to design the statistical and machine learning models that power key product lines, with work spanning applied research, production engineering, and clear communication of insights. The team culture values rapid learning with new AI tools and a bias toward building directly for customers.

Responsibilities - Apply data mining, statistical analysis, and machine learning techniques to produce objective, actionable insights that inform business and product decisions. - Collaborate with cross-functional partners across sales, marketing, and product to identify business opportunities and develop data-driven solutions that drive growth and engagement. - Partner with product managers, engineers, and fellow data scientists to drive data-informed decisions that produce meaningful business impact. - Communicate analysis, insights, and recommendations clearly to senior business partners in verbal, visual, and written formats. - Thrive in a dynamic, fast-evolving environment and adapt as priorities shift.

Requirements - Bachelor's degree in computer science, engineering, mathematics, statistics, economics, or another quantitative discipline; a Master's degree is preferred. - Two or more years of data science experience in an industry setting. - Solid statistical and machine learning foundations, including regression and classification, clustering, and offline and online model evaluation. - Experience with advanced machine learning techniques: supervised and unsupervised learning, graph algorithms, deep learning such as NLP, recommendation systems, and generative AI. - Proficiency in Python and SQL, along with ML frameworks like scikit-learn, TensorFlow, or PyTorch. - Experience with cloud tooling, preferably on AWS (SageMaker, DynamoDB, Athena, Glue or similar), and with workflow orchestration tools such as Airflow.

Nice to have - Experience building LLM applications including prompting, retrieval-augmented generation, and evaluation. - Production experience shipping machine learning systems at scale. - Familiarity with A/B testing and other experimentation methodologies for measuring product launches. - Software engineering best practices such as object-oriented programming, test-driven development, CI/CD, git, and shell scripting.

Benefits and work setup Hybrid work model that supports in-person collaboration while valuing individual flexibility. Compensation is structured by geographic zone, with disclosed base salary ranges of roughly $112,000–$179,000 in Zone A, $97,000–$155,000 in Zone B, and $87,000–$139,000 in Zone C (USD), plus potential bonus or overtime, equity, and benefits. Reasonable accommodations are available throughout the hiring process for candidates with disabilities.