Staff Data Scientist

The role:
The Compliance Staff Data Scientist will be responsible for assisting the Anti-Money
Laundering Compliance program with model development, model optimization, model
validation, management information reporting, AML system integration, AML data
infrastructure and AML data architecture to effectively fight financial crime. Additionally,
this role will also support AML governance initiatives including risk assessments and
internal/external inquiries.

What you’ll do:
- 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.

What you’ll need:
- Bachelor’s Degree or Master’s Degree in Statistics, Computer Science,
Mathematics, Finance, Computer Science, Engineering or other relevant areas.
- 6+ 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.

Nice to Have:
- 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