R&D Finance & Data Scientist

Job description

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. If you love thinking analytically and are passionate about using your financial knowledge to navigate challenges, we'd love to hear from you! As an R&D Finance & Data Scientist, you will play a critical role in architecting, building, and maintaining the financial data infrastructure and advanced analytical tooling that powers Apple’s R&D Finance organization. In this role, you will bridge the gap between financial operations and cutting-edge data science—optimizing how we plan, track, and manage R&D Headcount, Opex, and Capex spend. You will combine deep financial acumen with hands-on technical expertise in SQL, Python, machine learning, and Generative AI to eliminate operational friction, improve data governance, and deliver actionable executive intelligence. Partnering closely with cross-functional teams and the Finance Transformation Office, your work will directly influence operating margin performance, financial transparency, and strategic product roadmaps in a dynamic, fast-paced global environment. Minimum Qualifications: 5+ years of hands-on experience in full lifecycle software/tool development, data engineering, and analytics in a finance, operations, or enterprise analytics setting BS in Computer Science, Software Engineering, Data Analytics, Information Systems, Finance/Economics with a technical focus, or equivalent practical experience. Advanced proficiency in SQL and Python for complex data extraction, pipeline, orchestration, and analytical modeling across large-scale relational databases and data warehouses. Experience with front-end / web-based tools and scripting (e.g., JavaScript) to power internal financial workflows. Hands-on expertise building enterprise-grade data flows, ETL/ELT pipelines, and advanced analytics in Dataiku and relational environments (e.g., FileMaker, Snowflake, or modern data warehouses). Proven track record designing, building, and maintaining high-performance, interactive Tableau (or comparable BI) dashboards for spend analytics and executive-level reviews. Working knowledge of Machine Learning engineering and LLM development/orchestration frameworks (e.g., LangChain, LangGraph, AgentConnect). Demonstrated ability to implement AI/ML capabilities for anomaly detection, automated reconciliation, and continuous control monitoring Demonstrated experience leading end-to-end Project Lifecycle Deployments (scoping, technical architecture, prototyping, testing, deployment, and change management). Exceptional communication skills with the ability to translate complex technical architectures into clear, concise business narratives for executive stakeholders. Comfort with ambiguity, high ownership, and a proactive approach to pairing identified problems with viable technical solutions. Preferred Qualifications: Direct experience within R&D / Engineering Finance or tech-industry financial operations. Familiarity with RPA (Robotic Process Automation), enterprise ERP/financial planning platforms (e.g., SAP, Workday, Adaptive Insights), and enterprise data warehousing governance. Basic understanding of core finance and accounting principles (budgeting, forecasting, P2P lifecycle, Capex vs. Opex, headcount planning, and general ledger/cost center structures).