Services Finance, Data Scientist

As a member of the Services Finance Data Science and Engineering team, you will partner with various business and engineering teams to translate complex business requirements into data-driven solutions. You will demonstrate proficiency in using AI coding assistants (such as Claude Code, Codex, etc) and leverage agentic AI frameworks to rapidly prototype, iterate, and deploy analytical solutions. Success in this role requires combining strong statistical and machine learning fundamentals with the ability to effectively prompt, guide, and collaborate with AI tools to maximize productivity and innovation. Minimum Qualifications 5+ years of experience in a data science or related analytical role Bachelor's degree in applied mathematics, statistics, computer science, data science, economics, or related quantitative field Creative and curious thinker with ability to translate business problems into data requirements and actionable solutions Proven "builder" mentality: demonstrated ability to independently execute from idea to implementation, with track record of shipping production solutions with minimal oversight Excellent communication skills with ability to present complex findings to both technical and non-technical audiences Strong programming proficiency in Python or R, with demonstrated experience using AI coding assistants (e.g., Claude Code, GitHub Copilot) to accelerate development Expertise in SQL and data wrangling with large-scale datasets Strong foundation in statistical methods and machine learning, with experience applying these techniques to solve business problems Experience with prompt engineering and developing agentic AI workflows for automation and efficiency Preferred Qualifications Advanced modeling expertise: Time series forecasting, Bayesian methods, or anomaly detection Data visualization & storytelling: Experience with interactive visualization tools and report generation frameworks such as Shiny, Quarto, Streamlit, Tableau, etc. Data infrastructure proficiency: Modern data platforms (Snowflake, BigQuery, Spark), workflow orchestration (Airflow, GitHub Actions, etc.), and containerization (Docker) API development and integration: Building and consuming REST APIs, database connectivity Software engineering practices: Git, CI/CD pipelines, shell scripting, and production deployment experience