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This role is the Digital Asset Transaction Monitoring Lead Analyst supporting the design and deployment of suspicious activity monitoring models for digital assets within State Street's Financial Crimes Compliance function. The position focuses on hands-on involvement in model development, data analysis, and enhancement of monitoring capabilities across public blockchain and digital asset use cases.

This is a highly technical role requiring strong SQL and Python expertise, with practical experience in data analysis, model development, or feature engineering, rather than a reporting or AML operations-focused position. The successful candidate will contribute to building scalable models and analytical solutions, while leveraging experience in crypto/digital asset AML to strengthen monitoring of emerging financial crime risks. You will collaborate closely with business, data, and technology teams to support the delivery of data-driven solutions across the model lifecycle.

This role can be performed in a hybrid model, where you can balance work from home and office to match your needs and role requirements. Our standard hybrid model is 4 days on site and 1 day remote. Preferred location is Boston or Quincy, MA. We will also consider applicants from: Stamford, CT; Princeton and Clifton, NJ; Berwyn, PA.

The team you will be joining plays an important role in the overall success of the organisation. Across the globe, institutional investors rely on us to help them manage risk, respond to challenges, and drive performance and profitability.

In your role, you will contribute to building scalable, data-driven solutions that support Financial Crimes Compliance across emerging digital asset products. You will support the development and improvement of resilient, high-performing systems that enable effective monitoring of complex transaction behaviours. Join us if making your mark in the financial services industry from day one is a challenge you are up for.

What you will be responsible for

As Digital Asset Transaction Monitoring Lead Analyst, Assistant Vice President you will:

• Support AML and business stakeholders with the hands-on development and documentation of suspicious activity monitoring models

• Support model owner responsibilities, including involvement in model development, testing, tuning, implementation, and ongoing monitoring

• Be involved in designing and building features, scenarios, and analytical logic to enhance detection of suspicious digital asset activities

• Contribute to the development of user stories and requirements to improve monitoring of digital transactions and entities

• Contribute to improving efficiency in Financial Crimes Compliance processes through development of analytical and investigative tools

• Work with large datasets and partner with data teams to support data sourcing, transformation, and pipeline design (e.g. ETL processes)

• Collaborate with broader AML Compliance teams including policy, risk assessment, and KYC teams, ensuring alignment of data and model requirements

• Support validation and testing to ensure the accuracy and effectiveness of models and processes
  

Education & Preferred Qualifications

• 3+ years of experience in suspicious activity monitoring within Financial Crimes Compliance, with proven hands-on experience in data analysis and/or model development coupled with experience in digital assets sets you up for success in this role

• Hands-on experience with SQL, including querying, data manipulation, and working with large datasets is required

• Working knowledge of Python for data analysis, feature engineering, or model development is required

• Understanding of cryptocurrency transaction flows, blockchain data, and AML typologies in digital assets, or demonstrated interest in developing expertise in this area is required

• Familiarity with AML transaction monitoring frameworks, including exposure to model development, scenario design, or alert tuning

• Experience working with large datasets and data environments (e.g., Databricks, Spark, Snowflake)

• Experience utilising blockchain analytics tools (e.g., Chainalysis, TRM Labs, Elliptic) is preferred

• Bachelor's degree in a quantitative discipline (e.g., Mathematics, Statistics, Computer Science, Finance) or related field; advanced degree preferred