Job Title: AML Data Analyst Department: AML Compliance Location: US-MA-Quincy
Regional Marketing Statement
Our Company State Street Corporation (NYSE: STT) is the world's leading provider of financial services to institutional investors including investment servicing, investment management and investment research and trading. With $31.62 trillion in assets under custody and administration and $2.51 trillion in assets under management as of December 31, 2018, State Street operates globally in more than 100 geographic markets and employs over 40,000 worldwide. For more information, visit State Street's website at www.statestreet.com.
State Street Corporation is building a transformational Anti-Money Laundering (AML) program by executing innovative and effective data and transaction monitoring strategies. As an AML Data Analyst, you will help develop new capabilities across AML systems including the design and execution of our new enterprise AML data lake. In addition, you will help engineer state-of-the-art models, methods, and features required to support the new monitoring program. While building a new team, you will leverage leading technology to develop POCs and test software efficacy based on industry experience across the AML and bank risk technology space. You will also play a significant role in partnering with business units, data stewards, and IT teams for a better understanding of the customer journey across multiple lines of business and set the roadmap to support both business line transparency and AML compliance needs. Solutions developed by this multi-faceted team will support enterprise risk and business needs including transaction monitoring across our global business units as well as trade surveillance, market surveillance, ethics, and digital analytics.
Job Description
Major Responsibilities:
Support AML and business stakeholders with the development of analytic insights into data to support project deliverables.
Consume data from AML stakeholders including the FIU to translate qualitative AML risk factors into responsive risk models leveraging new technology
Synthesize data into features and analytic frameworks to support ongoing financial crime risk mitigation strategies
Support the application of economic, quantitative, and statistical skills in the development of models to support profitable growth while mitigating financial crime risk
Develop statistical models and algorithms to identify data anomalies and support risk identification efforts
Test and maintain data layer integrity while performing peer code reviews
Leverage strong business and communication skills to identify and resolve data gaps, code issues, while mining data for insights and patterns
Support ongoing data engineering and database development along with required data transformation activities
Qualifications
Education/ Certifications:
Bachelor's degree in a quantitative discipline, such as Mathematics, Physics, Statistics, Finance or related field, advanced degree preferred
6+ years of experience in data management, data forensics, reference or transactional data management, data governance, data engineering data architecture, data modeling or data analysis experience
Experience with MDM tools or products (e.g. SAP MDG, Informatica, etc)
Strong SQL, R, Python, experience required
Proficiency with Actimize, Fircosoft, AWS, Informatica, and related database tools and technology preferred
Proficiency with banking and financial markets preferred
Skills and Experience:
Experience working with data and analytic solutions and the science behind them, either as a builder/provider of these solutions or as a client that uses them.
Strong skills building positive business partner relationships
Strong networking skills and ability to work across multiple organizations to accomplish diverse goals
Demonstrated ability to influence critical business outcomes in a matrix based, global environment
Excellent verbal and written communication and collaboration skills to effectively communicate with both business and technical development teams
Comfortable working in a fast-paced, results-oriented environment
Familiarity with data privacy laws and controls (like: GDPR)
