State Street
Wealth Transaction Monitoring Lead Analyst, Vice President
Boston, Massachusetts, USA · Exec
Sponsorship not specified$110k-$189kDetected 6 hours ago
PythonSQLDatabricksData AnalysisData ScienceStatisticsComplianceAgileAuditingResearchCommunicationProblem SolvingTime ManagementInternal Audit
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odds of building a lasting career here
54Sponsors, lottery-bound
Cap-exempt (no lottery)0
Sponsors this role78
Entry-level history0
PERM / green-card track0
Lottery odds (Level III)83
Fits your clock70
Sponsors, but it's cap-subject — you still face the weighted lottery (~45% per draw at Level III). Good if you win; have a cap-exempt backup on your list.
Lottery odds assume a STEM candidate.
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About the role
- Direct broker-dealer experience is a core requirement for this role.
- This requires practical knowledge of brokerage products, customer and account structures, securities and cash transaction flows, trade lifecycles, clearing and custody processes, and broker-dealer-specific financial crime risks.
- You will play a significant role in partnering with broker-dealer and Wealth business stakeholders, data stewards, FCC, IT teams, Model Risk Management, Internal Audit, and regulators.
Responsibilities
- Ability to take ownership and initiative, negotiate, influence, build consensus, and successfully navigate a demanding and international environment within a leading global financial institution.
- Across the globe, institutional investors rely on us to help them manage risk, respond to challenges, and drive performance and profitability.
Requirements
- Strong hands-on proficiency in SQL and Python, with demonstrated ability to analyze large and complex broker-dealer, transaction, position, or reference datasets.
Nice to have
- Supporting FCC and Wealth business stakeholders in the development and documentation of suspicious activity monitoring models and scenarios.
- Producing clear, complete, and audit-ready documentation and communicating technical analysis to business, technology, model risk, audit, and regulatory stakeholders.
- Bachelor's degree in a quantitative discipline, such as Mathematics, Physics, Statistics, Finance, Computer Science, Data Science, or a related field
- an advanced degree is preferred.
- Prior experience as a model owner, product owner, scenario owner, or in a comparable model governance role is preferred.
- Proficiency with Agile software development methodology, Databricks, and related database tools and technology is preferred.
- Working knowledge of related financial crime subject matter, including KYC, customer risk assessment, sanctions, investigations, and suspicious activity reporting, is preferred.
- Relevant professional certification, such as ACAMS, is preferred.
Compensation
- $110,000 - $188,750 Annual
- incentive compensation including eligibility for annual performance-based awards (excluding certain sales roles subject to sales incentive plans)
Benefits
- Employees are eligible to participate in State Street's comprehensive benefits program, which includes: our retirement savings plan (401K) with company match
- insurance coverage including basic life, medical, dental, vision, long-term disability, and other optional additional coverages
- paid-time off including vacation, sick leave, short term disability, and family care responsibilities
Company info
- The role is specifically focused on mitigating financial crime risks arising from broker-dealer customers, brokerage accounts, securities activity, cash movements, and related Wealth products and services.
- We keep our clients at the heart of everything we do, and smart, engaged employees are essential to our continued success.
- We are committed to fostering an environment where every employee feels valued and empowered to reach their full potential.
Visa & Work Authorization
- gin, ancestry, ethnicity, age, disability, genetic information, sex, sexual orientation, gender identity or expression, citizenship, marital status, domestic partnership or civil union status, familial status, military a
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This listing is sourced directly from State Street's careers page and normalized into a canonical job model.