Note: The job is a remote job and is open to candidates in USA.
State Street is a global investment firm focused on helping institutional investors manage risk and drive performance. They are seeking an Officer-level Compliance Technology Business Analyst with expertise in AI and Machine Learning to support data-driven solutions in financial crime compliance, particularly in Anti-Money Laundering and Sanctions Screening.
Responsibilities
- 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
Skills
- Bachelor's degree with concentration in Engineering, Business or Technology preferred
- 1-2+ years of experience in financial services, including relevant responsibilities
- Experience in Financial domain is required: knowledge of Anti-Money Laundering (AML), Sanctions, Transaction Monitoring, Know Your Customer (KYC), financial securities, and trading principles
- Experience in FinTech working with SWIFT message types (specifically Swift - MT, MX message formats)
- Experience in FinTech working with Fedwires transactions
- Experience/Exposure to Financial Crimes Compliance - such as AML, Alert generation
- 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 common challenges in applied machine learning, such as data quality, class imbalance, and model interpretability
- Interest in or exposure to AML, sanctions, fraud, or risk analytics, particularly in regulated environments
- Ability to clearly explain analytical results and model behavior to technical and non‑technical stakeholders
- Strong problem‑solving skills, attention to detail, and a structured, analytical mindset
- Ability to work collaboratively in cross‑functional teams and learn from more senior data scientists and engineers
- Ability to manage multiple simultaneous tasks in a high pressure, deadline environment
- Ability to take ownership and initiative, to negotiate, influence and build consensus and successfully navigate within a demanding and international environment
- Strong skills in analytical thinking, problem solving, research, time management, and verbal and written communication
- Strong collaboration and relationship management skills
- Ability to work independently
- Be able to work in a complex, deadline drive