Technical SME Data Engineer

BLN24•Mclean, VA

7d•Hybrid

About The Position

BLN24 is seeking a Technical Subject Matter Expert (SME): Data Engineer to support CFPB's RegTech BPA program by turning large-scale regulatory data into actionable insight. This role requires deep experience implementing decision analysis and machine learning models in a cloud-native environment, strong expertise with Apache Spark, and the ability to translate organizational goals into working statistical and ML solutions that comply with federal AI governance requirements.

Requirements

5+ years of professional experience with decision analysis and machine learning algorithms (e.g., Decision Trees, Random Forests, Gradient Boosted Trees, Linear Regression, Collaborative Filtering, K-Means) implemented in a cloud-native environment
Experience with Spark's architecture and internals, core APIs, SparkSQL and other high-level data access tools, and Spark's streaming capabilities, or similar
Experience performing data cleaning tasks and creating comparative trend analysis on large-scale data
Experience translating organizational goals into working machine learning models, statistical models, or pattern recognition
Experience working with open-source and community solutions and using GitHub for source code management
Bachelor's degree in Mathematics, Data Science, or similar field; Master's degree in Mathematics, Data Science, or similar field strongly preferred

Nice To Haves

5+ Experience using Amazon Web Services EMR Spark
Experience with Zeppelin or similar interpreters
Familiarity with federal AI governance requirements, including model documentation and bias testing
Prior engagement at CFPB or other federal financial regulators (e.g., FDIC, OCC, SEC, FRB, NCUA)
Experience supporting a federal FISMA Moderate or comparable security-compliance environment

Responsibilities

Design, build, and implement decision analysis and machine learning models (e.g., Decision Trees, Random Forests, Gradient Boosted Trees, Linear Regression, Collaborative Filtering, K-Means) in a cloud-native environment
Develop large-scale data processing pipelines using Spark's core APIs, SparkSQL, and streaming capabilities
Perform data cleaning and create comparative trend analyses on large-scale datasets
Translate organizational goals into working ML models, statistical models, and pattern recognition solutions
Ensure all ML and AI model work complies with the Bureau's AI governance policy and OMB M-24-10 (or successor) documentation and bias-testing requirements
Contribute to open-source and community solutions, managing source code in GitHub
Collaborate with program, policy, and technical teams to communicate findings and model results.