Data Scientist- Finance AI Transformation, Global Fp&a Technology, Gft, Corp Fp&a

Amazon Amazon · Big Tech · Seattle, WA · Data Science

Data Scientist role focused on building AI capabilities for financial planning and analysis (FP&A) at Amazon. The role involves developing forecasting models and AI systems, including agents, to automate repetitive finance tasks. The models ship to production and influence key financial decisions like stock-based compensation and headcount planning. Responsibilities include applying data science methodologies, leading model lifecycles from research to production, conducting experiments, and partnering with stakeholders.

What you'd actually do

  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.

Skills

Required

  • data querying languages (e.g. SQL)
  • scripting languages (e.g. Python)
  • statistical/mathematical software (e.g. R, SAS, Matlab, etc.)
  • data scientist experience
  • machine learning/statistical modeling data analysis tools and techniques
  • applying theoretical models in an applied environment

Nice to have

  • Python
  • Perl
  • another scripting language
  • ML or data scientist role with a large technology company

What the JD emphasized

  • building AI capabilities that take on repetitive work in finance
  • build agents and AI systems that finance teams use directly
  • models you build here ship to production

Other signals

  • building AI capabilities that take on repetitive work in finance
  • build agents and AI systems that finance teams use directly
  • lead the end-to-end lifecycle of forecasting models — from research and experimentation through production launch