Sr. Analyst, Financial Crimes Analytics

Raymond James•Saint Petersburg, FL

2d•Hybrid

About The Position

This person is expected to be in office 50% of the time. (Half of the working days per month must be in-office) The Senior Analyst, Financial Crimes Analytics (FCA), will assist in developing and maintaining the Anti-Money Laundering (AML) control environment involving tuning, calibration, segmentation and optimization of various AML Models for regulatory compliance. They will play a critical role in identifying, assessing, and mitigating Financial Crime risks across our organization, with a heavy emphasis on SQL and Python expertise, database management, and analytical problem-solving. Reporting to the Senior Manager, Financial Crimes Analytics, this role will work with members of the Financial Crimes Management (FCM) team to identify AML trends, control enhancement opportunities, and perform root cause analysis of non-detected activities while also presenting these findings to AML/ senior leaders. Additionally, the Senior Analyst will regularly interact with Technology teams during the design and testing of new models.

Requirements

Knowledge of Financial crime risks, including money laundering, sanctions evasion, and regulatory expectations (e.g., BSA/AML, OFAC, FATF).
Relational database concepts such as normalization, indexing, partitioning, and query optimization.
Data management principles, including data quality, lineage, governance, and privacy standards.
Fundamentals of statistics and Machine Learning.
Skill in Writing complex queries, optimizing performance, and managing large datasets.
Scripting in Python, or similar languages to support data extraction and automation efforts.
Working across various databases such as Oracle, SQL Server, Redshift, Mongo DB, etc.
Conducting descriptive, diagnostic, and trend analysis on structured and semi-structured data.
Communicating technical findings clearly to both technical and non-technical audiences.
Ability to Translate ambiguous business questions into structured data analysis projects.
Work independently on complex problems while collaborating effectively across teams.
Manage multiple projects simultaneously with attention to deadlines, priorities, and accuracy.
Think critically and creatively to detect anomalies, emerging risks, and operational inefficiencies.
Adapt to rapidly changing regulatory, operational, or organizational environments.
Uphold the highest ethical standards when working with sensitive financial and customer data.
Bachelor’s degree (B.A.) from a four-year College or university or equivalent experience in Computer Science, Data Analytics, Business, Finance, MIS or related fields
Three (3) or more years’ experience working on technical teams in Anti-Money Laundering (AML) compliance, Cyber Investigations/Fraud, enterprise technology and/or analytics in financial services, banking, asset management, FinTech, or consulting
Any equivalent combination of experience, education, and/or training.

Nice To Haves

Industry-standard financial crimes systems (e.g., transaction monitoring, case management, entity resolution platforms) is a plus.
Data Ingestion tools such as Alteryx, AWS Glue, Informatica, etc.
Developing visualization reports using tools like Tableau, Qlik, Power BI, or Python libraries is a plus.
Amazon Web Service (AWS) certifications are a plus but not required.

Responsibilities

Write advanced queries to extract, transform, and analyze large datasets from complex databases.
Develop and enhance transaction monitoring, screening, and customer risk models.
Perform ad hoc and strategic analysis on financial crime trends, customer behaviors, and operational risks.
Work with large, sometimes messy, datasets ensuring data quality, consistency, and governance.
Create reports for stakeholders, regulators, and internal risk committees.
Translate business questions into analytical solutions and communicate findings clearly to non-technical stakeholders.
Collaborate with technology teams to define data requirements, optimize database structures, and support analytics infrastructure improvements.
Stay current with financial crime threats, emerging typologies, and regulatory expectations.