Services Finance, Data Scientist
Apple
Accounting & Finance, Data Science
Cupertino, CA, USA
USD 172,100-258,600 / year + Equity
Posted on May 15, 2026
Imagine what you could do here. At Apple, new ideas have a way of becoming extraordinary products, services, and customer experiences very quickly. Bring passion and dedication to your job and there's no telling what you could accomplish. At Apple, you’ll share in a commitment to excellence by partnering with a world-class team to create innovative products that delight customers. Finance is about fueling innovation. We do this by hiring quality individuals with integrity, personal accountability, teamwork, excellence, and proactive thinking. If you love thinking analytically and are passionate about using your financial knowledge to navigate challenges, we'd love to hear from you! The Services Finance Data Science and Engineering team is seeking a passionate and highly motivated data scientist who excels at the intersection of traditional data science and modern AI capabilities. You will play a key role in shaping the success of Apple’s current and future products by leveraging both foundational data science expertise and cutting-edge AI tools to accelerate solution development.
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.
- Partner with stakeholders to understand complex business problems and identify opportunities for data-driven impact
- Design and implement analytical frameworks that directly address business needs while leveraging AI tools to accelerate delivery
- Rapidly prototype solutions using AI coding assistants to validate approaches and gather early feedback
- Build and deploy production-ready models and automated analytics pipelines, ensuring scalability, reliability, and performance monitoring
- Conduct rigorous statistical analysis and model validation to measure impact and ensure solution effectiveness
- Create self-serve analytics tools and dashboards that empower stakeholders to make data-informed decisions independently
- 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
- 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