Artificial Intelligence & Machine Learning, Off (US Only)

Who we are looking for

We are looking for an Officer‑level Compliance Technology Business Analyst / AI / Machine Learning professional to support the development and operation of data‑driven solutions across Anti‑Money Laundering (AML), Sanctions Screening, and Financial Crimes Compliance platforms. This role is highly hands‑on and data‑science focused, working directly with large datasets, machine learning models, and analytics pipelines used to detect and prevent financial crime.

Individual will liaise with the project sponsors, stakeholders, external vendors and other divisions within State Street to document current state, elicit & document requirements to ensure the delivery of quality products. Individual must demonstrate leadership abilities in analysis, problem solving, documentation and communication. Individual must be able to work under minimal direction while keeping senior management apprised of issues relating to analysis work related deliverables.

The ideal candidate has strong quantitative and technical skills, a solid understanding of machine learning techniques, and an interest in applying AI in a regulated financial services environment. The individual will work closely with senior data scientists, engineers, compliance partners, and technology teams to build, test, and monitor models while ensuring solutions remain explainable, auditable, and compliant with regulatory expectations.

What you will be responsible for

As Compliance Tech. AI/Machine Learning you will

• Develop, test, and enhance machine learning and advanced analytics models supporting AML, sanctions screening, transaction monitoring, and alert prioritization.
• Perform hands‑on data analysis using large transactional and reference datasets to identify patterns, anomalies, and risk indicators related to financial crime.
• Support feature engineering activities, including data exploration, feature selection, and transformation to improve model performance and stability.
• Train, evaluate, and tune models using appropriate techniques and performance metrics (e.g., precision, recall, false‑positive reduction).
• Assist in validating model outputs by analyzing false positives, false negatives, and alert quality in partnership with compliance and operations teams.
• Contribute to model documentation, including assumptions, methodologies, limitations, and performance results, to support audit and regulatory review.
• Support ongoing model monitoring and performance tracking, identifying drift, degradation, or data quality issues and recommending remediation.
• Work with data engineering teams to understand data pipelines, resolve data issues, and ensure model inputs remain accurate and reliable.
• Participate in Agile delivery processes, contributing to user stories, testing activities, and release support for AI/ML solutions.
• Support User Acceptance Testing (UAT) by validating model behavior against business and compliance expectations.
• Stay current on emerging AI/ML techniques, open‑source tools, and industry trends relevant to financial crime and compliance analytics.
• Be able to translate complex ideas into cogent business requirements documents
• Be proficient creating BRDs and data mapping documents.
• Collaborate with cross-functional teams to identify requirements, provide guidance, ask and respond to questions and assist with resolving complex issues.
• Ensure that any gaps identified in the BRD are addressed and rectified by the relevant team.
• Support development and testing teams by answering questions and updating documentation as needed based on feedback.
• Be responsible for communication, resolution, and potential escalation of critical issues.
• Work with the Project Manager and Product Owner to create and manage user stories using Jira
• Support team in triaging issues found during testing.
• Be able to work in a complex, deadline driven organization on projects with minimal supervision.
• Analyze complex problems, derive options and solutions and present in an understandable manner to stakeholders, developers, testers and users at multiple levels.

What we value

These skills will help you succeed in this role

• Strong foundation in machine learning and statistical concepts, including supervised learning, basic unsupervised techniques, and model evaluation.
• Hands‑on experience with data analysis and modeling using tools such as Python and SQL.
• Experience working with large, complex datasets; exposure to financial or transactional data is a strong plus.
• Understanding of