Note: The job is a remote job and is open to candidates in USA. SoFi is a next-generation financial services company and national bank using innovative, mobile-first technology to help members reach their financial goals. The Compliance Senior Data Scientist will support the Anti-Money Laundering Compliance program through AML model development, optimization, validation, reporting, system integration, data infrastructure, and governance initiatives.
Responsibilities
- Facilitate AML model development, implementation, optimization, assessment and validation of risk-based customer screening, transaction screening, transaction monitoring and AML customer risk rating covering multiple product lines, including banking, brokerage and lending to ensure sound risk coverage across the enterprise
- Maintain, test and configure AML vendor solutions to ensure conceptually sound design, proper implementation, and acceptable model performance
- Research, compile and evaluate large sets of data to assess quality, integrity and completeness to determine suitability for AML model development
- Architect and lead the design of advanced AML models utilizing machine learning and statistical modeling methods for supervised and unsupervised learning
- Exercise flexibility in selecting model architectures, algorithms, third-party libraries, and development workflows, provided they align with project objectives and organizational requirements
- Ensure AML compliance and regulatory requirements are embedded in the model design
- Document modeling methodology, data sources, assumptions, and validation results
- Lead governance and quality control across the full AML model lifecycle including code reviews, validation of methodology, input data integrity, and performance metrics
- Ensure adherence to the organization’s established ML framework, coding conventions, documentation standards, and model risk management policies, embedding AML compliance and regulatory requirements into design and deployment
- Oversee documentation and review processes for internal model validation, external regulatory examinations, and cross-functional approvals, while supporting resolution of development blockers and coordinating with key stakeholders
- Develop governance documentation related to tuning efforts, parameter changes and data validation for AML transaction monitoring to ensure a comprehensive audit trail is maintained
- Track and report results of tuning and optimization activities and model performance to senior management
- Develop robust management information dashboards displaying real-time or near real-time AML metrics
- Partner with and advise the AML Governance Unit by providing necessary data for AML Risk Assessments, internal/external audit examinations and other regulatory requirements
Skills
- Bachelor's Degree or Master's Degree in Statistics, Computer Science, Mathematics, Finance, Computer Science, Engineering or other relevant areas
- 3+ years of experience in the finance industry focusing on BSA/AML, OFAC, or fraud modeling/analytics
- Statistical/data analytical skills, including data quality validation, and predictive modeling experience in SQL and Python
- Knowledge of and ability to leverage traditional databases, cloud-based computing, and distributed computing
- Track record of leading AML governance-related initiatives, such as risk assessments, internal/external audits and other regulatory requirements
- Demonstrated ability to communicate effectively with all levels of the organization and across different business lines
- Knowledge of AML regulations and the USA PATRIOT Act
- Familiarity with regulatory guidance on Model Risk Management (Federal Reserve SR Letter 11-7, OCC Bulletin 2011-12, FDIC FIL 22-2017, DFS504)
- Experience with data visualization (e.g., Tableau)
- Experience with data monitoring systems (e.g., DataDog, Monte Carlo)
- Experience with cloud data infrastructure (e.g., Snowflake)
- Experience with automated transaction monitoring (e.g., Verafin)
- Experience with customer/transaction screening (e.g., LexisNexis)
- Experience with infrastructure automation software (e.g., Terraform)
- Familiarity with virtualization and containerization (e.g., Docker)
- Familiarity with container orchestration (e.g., Kubernetes)
- CAMS certification preferred
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