# Data Scientist, AWS Payments and Fraud Prevention at Amazon

Amazon is hiring a Data Scientist, AWS Payments and Fraud Prevention in Berlin, Germany. Level rates it Builds AI ●●●●; you can [apply on Amazon](https://jobsbylevel.com/go/06625fad-e278-4853-ba98-88338fda2214?utm_medium=ai-agent).

AI Level 4, AI centrality 88 out of 100. DE, BE, Berlin.

## Details

- Company: [Amazon](https://jobsbylevel.com/companies/amazon)
- AI level: AI Level 4 (score 88 out of 100)
- Location: DE, BE, Berlin
- Posted: October 12, 2026
- Apply: https://jobsbylevel.com/go/06625fad-e278-4853-ba98-88338fda2214?utm_medium=ai-agent
- Free CV review for this job: https://jobsbylevel.com/cv-review?job=data-scientist-aws-payments-and-fraud-prevention-at-amazon-db2277

## Prepare for this job

The job page lists 5 likely interview questions for this offer, with a CV checklist: https://jobsbylevel.com/jobs/data-scientist-aws-payments-and-fraud-prevention-at-amazon-db2277

## Description

Own the detection models and the measurement science that find fraudulent and compromised AWS accounts in real time. Join a team of scientists and engineers whose analysis decides how fraud is detected and stopped, and how fast. The AWS Payments and Fraud Prevention organization keeps AWS a safe place to build for millions of customers. Our team owns the science behind fraud and compromise detection on AWS. Account compromise is the case where a legitimate customer's credentials are stolen or exposed and an attacker uses the account to run unauthorized workloads. Fraud is the case where an account exists to consume resources with no intent to pay. Most accounts our models score belong to legitimate customers, so every decision has to stop fraud while leaving their work undisturbed. As a Data Scientist you will own detection models end to end, from the first look at an emerging pattern through feature design, training, shadow evaluation, production launch and the weekly performance review. You will define how we measure ourselves: precision by outcome, time to detect, runtime of unauthorized resources, and the cost a model prevented. - Own models that detect and block fraudulent activity in real time, protecting customers and AWS. - Turn a pattern spotted in a handful of accounts into a model that screens millions. - Define the metrics your models and systems are measured by and discuss them in weekly and monthly business reviews. - End-to-end ownership, from the first signal to the enforcement action and the cleanup after it. Key job responsibilities - Own detection models and rules end to end: frame the problem with partners, source and engineer features from large-scale activity, sign-in and usage data, train and validate, run shadow evaluations, launch, and monitor performance after launch. - Investigate new fraud and compromise patterns as they emerge, quantify their scale and design the detection response, from a fast rule to a trained model, with the trade-off between speed and precision stated. - Define and audit the metrics that measure our systems: outcome-based precision and recall, time to detect, runtime of unauthorized resources, prevented cost. Own the analysis behind weekly and monthly business reviews. - Evaluate agentic and LLM-based systems: build labeled evaluation sets, measure error rates in both directions, and set the thresholds under which automation is allowed to act. - Extend detection to new AWS services as they launch, including AI services where spend accrues in minutes. - Write clear technical documents for scientists and for non-technical partners in operations, engineering and leadership, and present them in science reviews. - Mentor junior scientists and analysts on method, and help interview for the team. A day in the life You begin with the performance review of the models you own: what they caught, what was later overturned, and what slipped past every layer. You pull the misses, find that three share a pattern you have not seen before, size it in the data, write a shadow-mode rule, and put a one-page analysis in front of the team with the precision you expect and the cost of waiting for a full model. In the team's planning session for the next quarter, that analysis becomes a proposal for a new model and you make the case for its place on the roadmap against the other layers of the detection stack. You also audit the cases your agentic system resolved on its own and adjust its evaluation set and thresholds. You use AI coding assistants daily, for SQL, Python and documentation, and you check their work. Your stakeholders are the fraud engineering teams that power your models, the teams that act on their decisions, the AWS service teams whose customers we protect, and the leadership that reads your numbers every week. About the team We are a team of scientists, analysts, software and data engineers and a program manager, with close partners across AWS Fraud Prevention worldwide. We own the

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Source: https://jobsbylevel.com/jobs/data-scientist-aws-payments-and-fraud-prevention-at-amazon-db2277

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