Who we are looking for
This role is an AML Transaction Monitoring Lead Analyst responsible for the continued design, development, testing, implementation, and ongoing monitoring of suspicious activity monitoring models and analytical solutions within the Financial Crimes Compliance function.
The successful candidate will combine quantitative and analytical expertise with practical knowledge of AML transaction monitoring, financial products, customer activity, and regulatory expectations.
The role requires hands-on experience in data analysis or data modeling and the ability to translate AML typologies and business risks into clear, scalable monitoring approaches.
You will help develop effective models, methods, features, and proofs of concept; evaluate data and technology solutions; and document, monitor, and report model performance to key stakeholders.
You will also partner with business units, AML subject matter experts, data stewards, technology teams, and operational stakeholders to understand customer and transaction journeys across multiple lines of business and align business priorities with Financial Crimes Compliance requirements.
Why this role is important to us
The team plays an important role in strengthening the organization’s ability to identify, assess, and manage financial crime risk.
In this role, you will support the development of practical, transparent, and scalable AML monitoring solutions; coordinate across global stakeholders; and help ensure that model and analytical deliverables are well controlled, clearly documented, and fit for purpose.
What you will be responsible for
Support AML and business stakeholders in the design, development, testing, implementation, documentation, and enhancement of suspicious activity monitoring models and analytical solutions.
Perform model owner or model lead responsibilities, including development documentation, implementation planning, change governance, performance assessment, and ongoing monitoring.
Translate AML typologies, regulatory red flags, business risks, and operational requirements into model features, scenario logic, user stories, and analytical specifications.
Conduct data exploration, profiling, feature analysis, segmentation, threshold or parameter assessment, testing, and results interpretation using appropriate quantitative techniques.
Assess model effectiveness, limitations, assumptions, and performance; develop metrics and evidence to support model governance, challenge, validation, audit, and regulatory review.
Partner with business, operations, investigations, KYC, sanctions, risk assessment, data governance, model risk management, and technology teams to identify appropriate data sources and monitoring solutions.
Drive process efficiency and investigative effectiveness through workflow analysis, analytical tool design, automation, and proof-of-concept development.
Communicate complex analytical findings, risks, and recommendations clearly to technical and non-technical stakeholders, including senior management.
Maintain high-quality, auditable documentation and support timely remediation of identified model, data, or control issues.
Provide guidance and technical leadership to analysts and project teams and contribute to the development of consistent AML modeling standards and practices.
What we value
Strong analytical thinking, problem solving, research, and quantitative reasoning skills.
Solid understanding of AML transaction monitoring concepts, suspicious activity typologies, customer and transaction risk, and the broader financial crime compliance framework.
Ability to connect business domain knowledge with data, model design, and monitoring outcomes across financial products and services.
Ability to produce high-quality work products under regulatory, audit, governance, or management deadlines.
Strong written and verbal communication skills, with the ability to explain technical concepts and analytical results in clear business language.
Demonstrated ownership, sound judgment, attention to detail, and the ability to negotiate, influence, and build consensus across a complex global organization.
Ability to manage multiple priorities and work effectively with cross-functional and geographically distributed teams.
Education and preferred qualifications
Bachelor’s degree in a quantitative or related discipline, such as Mathematics, Statistics, Data Science, Computer Science, Economics, Finance, Engineering, or a comparable field; an advanced degree is preferred.
Three or more years of relevant experience in AML transaction monitoring, quantitative risk, financial crime analytics, data analysis, model development, or a related discipline.
Demonstrated experience with data analysis, data modeling, risk identification, model testing, or performance monitoring in a regulated financial services environment.
Strong hands-on SQL skills and working proficiency in Python or a comparable analytical programming language.
