Are you interested in changing the way accounting and finance works at Amazon? We are a science and engineering team leveraging ML models and LLMs to solve real-world problems faced by accountants and financial analysts.

We are part of the Amazon Financials Foundation Services (AFFS) organization. AFFS is responsible for processing and managing billions of financially relevant transactions sent globally from across Amazon each day, including orders, shipments, payments, and inventory movements. AFFS is at the center of Amazon's key initiatives and fuels the growth of Amazon's businesses worldwide by ensuring that businesses can easily integrate with our services and that accountants and financial analysts have the right tools to use our data.

As an Applied Scientist, you'll work alongside domain experts, engineers, and other scientists to understand business problems, propose scientific solutions, and deploy them to production. You'll work on scientific initiatives for accelerating reconciliation, standardization, and onboarding. This includes:
- Developing ML models or leveraging foundation models to produce proactive insights.
- Leveraging LLMs to answer questions asked during month-close reconciliation research.
- Run experiments over large datasets for new predictive learning approaches for financial analysis.
- Establish scalable, efficient, automated processes for large-scale data analysis, machine learning model development, model validation, and serving.
- Developing training/evaluation datasets for model fine-tuning.
- Collaborate with engineering to productionalize research.

Specific examples of this work include developing anomaly detection models to identify deviations in payments, applying generative AI to answer key in financial documents or business onboardings, and fine-tuning LLMs to provide recommendations on next steps.

As an interdisciplinary team, we maintain a balance between scientific research and productionalization. This means, you'll get a unique opportunity to influence the global scientific community by publishing papers externally and internally while also seeing your work used on a daily basis to help close the books and provide near real-time insights into Amazon's financials. You'll also have exposure to senior leadership and influence the scientific roadmap of the team.

You will need to have a start-up like mindset, as you will be working an in a highly iterative and collaborative environment with SDEs, Product Managers, and Accounting stakeholders to propose ideas, experiment, and scale rapidly. You should have a keen eye for what a good user experience should look like, possess excellent written and verbal communication, and have a keen interest in learning about accounting and financial processes.
- 3+ years of solving business problems through machine learning, data mining and statistical algorithms experience
- PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience
- Experience in patents or publications at top-tier peer-reviewed conferences or journals
- 3+ years of programming in Java, C++, Python or related language experience
- Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing- Experience using Unix/Linux
- Experience in professional software development
- Experience with finance or accounting
- Experience with TypeScript and React

Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, disability, age, or other legally protected status. If you would like to request an accommodation, please notify your Recruiter.

The base salary for this position ranges from $149,300/year up to $249,300/year. Salary is based on a number of factors 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. Applicants should apply via our internal or external career site.