Data Scientist- Finance AI Transformation, Global FP&A Technology, GFT, Corp FP&A

About the role

GFT team is a Finance Technology team within Corp FP&A. On our team, we enjoy a unique vantage point into everything happening within Amazon. As part of that, this role would be part of a team that is responsible for Company's enterprise-wide financial planning & analytics environment. The data flowing through our platform directly contributes to decision-making by our CFO and all levels of finance leadership. If you're passionate about building tools that enhance productivity, improve financial accuracy, reduce waste, and improve work-life harmony for a large and rapidly growing finance user base, come join us!

Requirements

• 3+ years of data querying languages (e.g. SQL), scripting languages (e.g. Python) or statistical/mathematical software (e.g. R, SAS, Matlab, etc.) experience
• 2+ years of data scientist experience
• 3+ years of machine learning/statistical modeling data analysis tools and techniques, and parameters that affect their performance experience
• Currently has, or is in the process of obtaining, a Ph.D. in Math, Statistics, Computer Science, or related science field
• Experience applying theoretical models in an applied environment
• Experience in Python, Perl, or another scripting language
• Experience in a ML or data scientist role with a large technology company
• Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.
• The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based

Additional Information

Join Amazon's Global FP&A Technology (GFT) team as a Data Scientist, where you'll shape the future of FP&A operations by building the forecasting and AI capabilities behind Amazon's financial planning. As part of GFT's mission to streamline, optimize, and drive financial excellence through technology, you'll work closely with Corporate FP&A teams and cross-functional stakeholders to develop scalable data science solutions that support strategic finance objectives. Your work will contribute directly to enhancing reporting, planning, cost allocations, and business enablement tools that empower finance teams across the organization. The models you build here ship to production and directly influence how Amazon plans its finances. Our forecasts drive stock-based compensation - a P&L expense of over $20B a year - and the headcount outlook spanning Amazon's ~1.5M employees, feeding the OP1, OP2, and Board-level planning cycles. A growing part of our charter is building AI capabilities that take on repetitive work in finance. Today this work takes many manual hours per cycle, spans disconnected tools and relies on repeated handoffs. This is the forward-looking edge of GFT, and a space where you can build agents and AI systems that finance teams use directly. We are looking for a results-driven scientist to join our team in Seattle. You will work with FP&A teams, product managers, software engineers, data engineers, UX designers, and front-end engineers to understand key requirements, and you'll lead the design and development of data science products and services. The ideal candidate brings strong problem-solving skills, stakeholder communication skills, and the ability to balance technical rigor with delivery speed and customer impact. This is a great opportunity for someone looking to drive the next generation of data science architecture, design, and development that scales to support Amazon's business growth and the FP&A function. Key job responsibilities 1. Apply a range of data science methodologies - statistical modeling, machine learning, and time series analysis - to solve complex forecasting challenges. 2. Lead the end-to-end lifecycle of forecasting models - from research and experimentation through production launch - including defining success metrics, obtaining stakeholder sign-off, and managing rollout in collaboration with software and data engineering. 3. Run large-scale exploratory data analysis and rigorous experiments at scale to uncover patterns, evaluate models, and improve performance. 4. Partner with finance stakeholders, engineers, and other scientists to understand customer needs and deliver solutions that meet them. 5. Translate complex research findings into clear, factually correct documents and explain technical concepts to technical and non-technical audiences.

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