Senior Machine Learning Engineer - Fraud Detection

RESPONSIBILITIES: Design, develop, and deploy machine learning models that improve fraud detection accuracy and system performance. Lead experimentation initiatives, including model development, offline evaluation methodologies, and production validation frameworks. Build and enhance scalable ML infrastructure and pipeline components to enable high-velocity experimentation. Develop and optimize distributed machine learning workflows using large-scale datasets and Spark-based environments. Partner closely with Product, Engineering, and Risk stakeholders to translate business challenges into impactful ML solutions. Champion best practices across testing, monitoring, documentation, and model lifecycle management. SKILLS AND EXPERIENCERequired 4+ years of industry experience developing production machine learning solutions. Strong hands-on experience with Python, SQL, and Apache Spark. Proven track record of designing experiments and building machine learning solutions from first principles rather than primarily consuming third-party or foundation models. Deep understanding of machine learning theory, statistics, model evaluation, and experimental design. Experience working with large-scale distributed data and compute environments. Ability to take ownership of projects from research through deployment and ongoing optimization. Strong communication skills with the ability to work cross-functionally across technical and business teams.

Preferred: PhD or Master's degree in Computer Science, Statistics, Mathematics, Machine Learning, or a related quantitative field. Experience in fraud detection, risk modeling, payments, financial technology, or transaction-based decision systems. Background in ensemble modeling, classification systems, or large-scale predictive analytics.

BENEFITS: Competitive base salary up to $21]00,000 Annual performance bonus