Responsibilities

• Own the team's highest-priority modeling problems end to end, framing ambiguous questions and delivering rigorous solutions on tight timelines
• Design, build, validate, and deploy advanced models spanning customer segmentation, digital marketing, and AI-enabled and agentic alert systems
• Translate business objectives into well-scoped modeling problems with clear success criteria and measurable outcomes
• Apply machine learning, statistical, and mathematical methods to develop novel solutions—including unsupervised methods such as PCA and k-means, supervised methods such as regression, tree-based models, and gradient-boosted algorithms, as well as causal inference, forecasting, and simulations
• Build applied AI and agentic solutions, including large language model-based systems, prompt engineering, and developer and AI assistants
• Serve as a technical lead on complex workstreams, helping direct and prioritize team efforts and unblocking others
• Communicate results, insights, and recommendations clearly to business stakeholders, technology partners, and senior leadership
• Partner with data science, engineering, product, and design teams to ensure solutions are feasible, scalable, and production-ready
• Mentor and guide teammates on modeling approaches, code quality, and analytical rigor
• Manage multiple engagements by prioritizing work, tracking dependencies, and escalating risks and tradeoffs proactively

Requirements

• Formal training or certification on data science concepts and 3+ years applied experience
• Undergraduate degree in a quantitative discipline or equivalent practical experience
• Strong programming skills in Python, including the modern data science and machine learning ecosystem
• Deep machine learning expertise across the full model lifecycle—feature engineering, training, validation, deployment, and monitoring
• Strong statistical and mathematical foundation, with the ability to select and apply the right technique to novel and ambiguous problems
• Applied AI engineering experience, including building large language model-based and/or agentic solutions and prompt engineering
• Proficiency with SQL and data querying at scale
• Demonstrated ownership mindset and ability to operate independently in ambiguous, fast-moving problem spaces
• Strong written, verbal, and presentation skills, with experience communicating effectively with both business and technology audiences, including senior leadership
• Ability to guide, mentor, and influence teammates and cross-functional partners

Preferred

• Graduate degree (Master's or PhD) in a quantitative discipline
• Experience in financial services or retail banking
• Experience with modern data platforms such as Snowflake or Databricks
• Experience productionizing models and integrating them into business workflows
• Familiarity with experimentation, model lifecycle management, and machine learning operations practices

Benefits

• comprehensive health care coverage, on-site health and wellness centers, a retirement savings plan, backup childcare, tuition reimbursement, mental health support, financial coaching and more