Data Scientist, Devices and Services FinTech
Job ID: | Amazon.com Services LLC
Are you looking for an opportunity to own a large-scale technology problem? Do you enjoy finding patterns and pushing the boundaries of current possibilities? Are you interested in building reliable and scalable systems that support Amazon's growth? If so, Amazon Devices and Services Finance Technology (FinTech) is the perfect place for you!
About this role
The Amazon Devices and Services FinTech team is expanding our data science team that is building forecasting solutions and advanced analytics platforms for the Amazon Devices and Services Finance organization, and we are looking for a Data Scientist to join us. As a data scientist, you will dive deep into data from across Amazon's finance organization, extract new insights, drive investigations and algorithm development, and interface with technical and non-technical customers. You will leverage your data science expertise and communication skills to pivot between delivering science solutions, translating knowledge of finance and operational processes into forecasting models, building product analytics for our AI systems, and communicating insights and recommendations to audiences of varying levels of technical sophistication in support of specific business questions, root cause analysis, planning, and innovation for the future.
Key Responsibilities
- Create various forecasts, including but not limited to Operational Expenses, and drive adoption of these forecasts by various teams within Amazon for financial and operations planning
- Build product metrics, monitoring pipelines, and analytics tools for our AI assistant to track performance, user engagement, and system reliability
- Continuously innovate through research and the application of the latest machine learning techniques to drive forecasting accuracy improvement and AI system optimization
- Perform exploratory data analysis to identify business opportunities and develop plans to address them across both financial forecasting and AI product development
- Communicate verbally and in writing to business customers with various levels of technical knowledge, educating them about our systems, as well as sharing insights and recommendations
- Build customer‑facing reporting tools and dashboards to provide insights and metrics which track forecast performance, AI assistant effectiveness, and explain variance
- Utilize code (Python, R, Scala, SQL, etc.) for analyzing data and building statistical and machine/deep learning models for both financial forecasting and AI product analytics
A day in the life
As a data scientist at Amazon Devices and Services FinTech, you'll dive into complex datasets using advanced analytics and machine learning to build accurate forecasting models for problems like Operational Expense forecasting. You'll also develop product metrics and monitoring pipelines for our AI assistant, analyzing user engagement and system performance. Collaboration with business and engineering teams is essential as you translate insights into actionable recommendations. Throughout the day, you'll innovate by adapting new forecasting methods and AI analytics approaches, ensuring solutions are scalable and reliable. Your communication skills help you solve unstructured problems in our fast‑paced, dynamic environment.
About the team
Amazon Devices and Services FinTech is the global team that designs and builds the financial planning and analysis tools for a wide variety of Devices' new and established organizations. From Kindle to Ring and even new and exciting companies like Kuiper (our new interstellar satellite play), this team enjoys a wide variety of complex and interesting problem spaces. We also own and operate the organization's AI assistant, making us true FinTech innovators embedded within Amazon.
Basic Qualifications
- Bachelor's degree in a quantitative field such as statistics, mathematics, data science, business analytics, economics, finance, engineering, or computer science
- 3+ years of data scientist experience
- 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
- 3+ years of machine learning/statistical modeling data analysis tools and techniques, and parameters that affect their performance experience
- Experience applying theoretical models in an applied environment
Preferred Qualifications
- Master's degree in a quantitative field such as statistics, mathematics, data science, business analytics, economics, finance, engineering, or computer science
- 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.
Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.
Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $125,500/year in our lowest geographic market up to $212,800/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job‑related knowledge, skills, and experience. Amazon is a total compensation company. Dependent on the position offered, equity, sign‑on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, please visit This position will remain posted until filled. Applicants should apply via our internal or external career site.
Posted: November 20, 2025 (Updated 5 days ago)
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