R&D Finance & Data Scientist

Apple•Apply•Published 1 days ago•First seen 1 days ago

Summary

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.

Description

The R&D Finance Tools & Analytics team is dedicated to modernizing and transforming core finance operations across the R&D organization. We architect internal tools, automate mission-critical workflows, and build advanced analytics platforms that enable financial analysts and leadership to make faster, data-backed decisions.

In this role, you will take ownership of identifying manual bottlenecks and architecting scalable, automated solutions across:

Financial Operations & Control: Redesigning and automating expense management, annual budget planning, Purchase-to-Pay (P2P) workflows, accounting close activities, and headcount approval pipelines.
Advanced Analytics & AI Enablement: Evaluating and deploying cutting-edge technologies—with a strong focus on Generative AI and automated monitoring—to conduct financial reconciliations, detect spend anomalies, and enforce internal controls at scale.
Data Governance & Product/LOB Modeling: Enhancing and maintaining the Line of Business (LOB) data models and multi-dimensional financial views to deliver granular, reliable visibility into product engineering investments.

Our team thrives on innovation, continuous improvement, and cross-functional collaboration. You will work side-by-side with finance analysts, business partners, and engineering teams to champion standardizations and scalable best practices across the enterprise.

Responsibilities

Systems & Process Roadmap: Own, prioritize, and drive the multi-year technology and process re-engineering roadmap for R&D Finance systems.
Architecture & Development: Architect, develop, test, deploy, and maintain robust data pipelines, internal web tools, and automated workflows supporting day-to-day finance operations.
Gen AI & Intelligent Automation: Build, fine-tune, and deploy LLM-driven agents and ML models (using frameworks like LangGraph / LangChain) to automate complex reconciliations, transaction categorization, and variance analysis.
Executive Dashboards & BI: Design and maintain scalable Tableau reporting suites for spend analysis, forecast-vs-actual variance, and executive leadership reviews.
LOB & Reporting Models: Enhance, optimize, and govern the LOB reporting data model to ensure seamless financial visibility across product portfolios.
Stakeholder Alignment & Requirements: Partner with SMEs, business leads, and finance analysts to gather business requirements, translate them into technical specs/prototypes, and drive consensus.
Change Management & Adoption: Lead rollouts, documentation, and user enablement for all newly developed tools and automated capabilities.
Confidentiality & Compliance: Uphold the highest standard of data integrity, confidentiality, and governance around sensitive product roadmaps and financial data.

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).