Principal Data Science Engineer - Financial Crimes

Fidelity Investments•Jersey City, NJ

1d•$107,000 - $216,000

About The Position

Financial Crimes Models & Analytics is seeking a Principal Data Science Engineer to lead the design, development, and optimization of our transaction monitoring surveillance models. You will partner with our Data Scientists, Business Intelligence analysts, Investigators, and Compliance professionals to translate regulatory requirements into scalable, rules based & machine learning detection models. This role requires a unique blend of banking, brokerage, anti-money laundering (AML) & fraud knowledge, as well as data analysis, data engineering, and AI/ML expertise. If you’re passionate about feature engineering, analytics, and machine learning to help fight financial crime, this is an excellent opportunity for you!

Requirements

Bachelor’s degree in Computer Science or equivalent technical discipline.
6+ years of experience in software or data engineering, including leading and delivering complex projects.
Strong proficiency in Python or at least one object-oriented programming language (e.g., Java) with a focus on writing clean, modular, and testable code.
Strong experience querying relational databases (e.g., Oracle, Snowflake) and working with non-relational databases (e.g., MongoDB).
Hands-on experience with machine learning algorithms, including decision trees, neural networks, regression models, clustering, and anomaly detection.

Nice To Haves

Prior experience working with customer and transactional data in the fraud or AML space.
Understanding blockchain technologies; prior experience in cryptocurrency monitoring is a plus.
Experience with dbt (data build tool) for data transformation and pipeline development.
Prior experience developing solutions using large language models (LLMs), including Retrieval-Augmented Generation (RAG) for information retrieval and workflow automation.
Certifications such as CAMS (Certified Anti-money Laundering Specialist) or CFE (Certified Fraud Examiner) are desirable

Responsibilities

Collaborate with team members and compliance partners to understand AML typologies and red flags we must detect
Assist in building detection models and features using SQL, Python, DBT (Data Build Tool), and Snowflake
Develop detection models using both rules-based and machine learning algorithms on customer, account, and transaction data
Apply machine learning and AI techniques to enhance suspicious activity detection by analyzing and identifying appropriate target data.
Monitor and optimize model performance using proper ML Operations tools
Help drive AI use cases for investigative workflows including integration in alert management systems, narrative generation, and straight through SAR filing
Champion best practices for CI/CD, robust automated testing, model performance, and production monitoring
Provide technical leadership, mentoring and training to other team members through code reviews, collaboration, and educational presentations
Explore new technologies (e.g., anomaly detection, graph analytics, predictive modeling) and determine their applicability to the team’s use cases; orchestrate the adoption of such technologies and trends where appropriate