Applied Scientist II, Risk And Compliance Solutions (RCS)

Can a large language model read a regulatory change and draft the requirements and control tests that follow from it, accurately enough for a Compliance Officer to sign off? How do you train a breach-risk model when confirmed breaches are rare, arrive months after the event, and mean different things to Compliance, Assurance, and the regulator? What does a model have to prove before a Compliance team acts on its output?

We are looking for an Applied Scientist to join the Data and Analytics team within Risk and Compliance Solutions (RCS). Our team builds the data, analytics, and modeling layer that RCS runs on, from regulatory reports and filings to the intelligence that tells Compliance Officers and our first line of defense (1LoD) partners where risk concentrates and which controls hold. You build the machine learning (ML) and generative artificial intelligence (GenAI) systems in that layer, take them into production, and own the evaluation science that lets Compliance rely on them.

The work spans 3 areas: risk models that score which open risks and control weaknesses convert to breaches; GenAI systems that remove manual effort from requirement translation, control testing, and regulatory reporting, with human approval gates at every stage; and the evaluation science behind every model and agent the team ships. Financial crime compliance is the first domain, and its constraints shape the science: threats change faster than control sets adapt, and every output faces internal audit and external examination. Once an approach proves out, you carry it into the next compliance domain across the RCS portfolio.

You own problems from formulation to production: framing the science question with Compliance and 1LoD partners, designing experiments and backtests, building and validating models, and shipping them into the pipelines, reports, and tools that RCS teams use. The stack includes Amazon SageMaker, Amazon Bedrock, Amazon Redshift, and AWS Lambda, and your partners include data scientists, data engineers, business intelligence engineers, and software development engineers across RCS.

The successful candidate treats an auditor's question as a design requirement, writes production-quality code alongside their models, and stays comfortable when the evaluation set does not exist until they build it.

Key job responsibilities
• Formulate ambiguous compliance problems as ML and GenAI problems with Compliance and 1LoD partners, and choose methods that fit the data, the risk, and the audit standard
• Develop risk models that learn from rare, delayed, and noisy labels to score which risks and control weaknesses convert to breaches, and calibrate them so a score carries a defensible meaning
• Build GenAI systems that remove manual effort from requirement translation, control testing, and regulatory reporting, with human approval gates at every stage
• Design the evaluation for every ML and GenAI system you ship: golden sets that compliance experts label, metrics suited to rare outcomes and generated text, and release criteria a system meets before Compliance relies on it
• Deploy and monitor models in production with software development engineers, including drift detection, performance tracking against realized outcomes, and retraining criteria
• Explain model decisions, quantify uncertainty, and document methodology so Compliance Officers, auditors, and examiners can trace how a system reached each output
• Create reusable science components, such as feature sets, scoring services, and evaluation harnesses, so an approach that works in financial crime compliance extends to the next domain without a rebuild
• Review peer models for statistical soundness, mentor data scientists and engineers on ML methods and experimental design, and publish or patent where the work and confidentiality allow


About the team
Risk and Compliance Solutions (RCS) serves as a crucial safeguard for Amazon's buyers, brands, and selling partners while ensuring business teams meet their regulatory obligations. RCS operates as the second line of defense under the Three Lines of Defense (3LoD) model, providing specialized oversight, monitoring, and expertise while setting policies and procedures that guide Amazon's compliance framework.

RCS's mission is to apply specialist industry insights and Amazon's unique approach to scale through technology, delighting customers through tailored compliance solutions. Our work spans multiple critical areas, including:
• Streamlined license acquisition that enables Amazon's financial services expansion
• Management of existing business risks across the organization
• Comprehensive governance, risk, and assurance services
• Innovative solutions such as Compliance as a Service (CaaS)
• A proactive, intelligence-led framework for compliance operations

Basic qualifications

• 3+ years of building models for business application experience
• Experience programming in Java, C++, Python or related language
• Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing

Preferred qualifications

• Experience in patents or publications at top-tier peer-reviewed conferences or journals
• Experience in financial modeling, or experience designing and implementing controls or experience performing audits over financial reporting environment
• Experience with training and deploying machine learning systems to solve large-scale optimizations, or experience with data infrastructures: relational analytic DBMS, Elastic-Search, and Big Data EMR/EC2/Glue/Lambda

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