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ML Lead @Capital One | Fraud Detection & Anti-Abuse Systems | Graph ML, User Behavior Modeling, Customer Embeddings | $50M+ Fraud Loss Prevention | ICML & ICLR Reviewer
Machine Learning Lead with 10+ years of experience building and scaling production machine learning systems for high-impact, real-world applications. At Capital One, I lead R&D initiatives focused on large-scale fraud detection and anti-abuse systems. My work spans user behavior modeling, graph-based feature engineering, and customer embeddings to uncover complex fraud patterns across Card products. By partnering closely with product, risk, and engineering teams, I have led the development of multiple production ML models that proactively detect fraud, contributing to over $50M in annual fraud loss prevention. Previously, at Oracle Health, I contributed to the Population Health platform, developing scalable data pipelines and improving platform performance for Healthcare clients. My expertise spans machine learning, graph-based modeling, data engineering, and production ML systems. I am particularly interested in translating advanced research into scalable, reliable systems that drive measurable business outcomes.
Kansas State University
Master of Science (with Thesis), Computer Science (Machine Learning, Bioinformatics)
August 1, 2007 – January 1, 2010
Rajiv Gandhi Prodyogiki Vishwavidyalaya
Bachelor of Engineering - BE, Computer Science
August 1, 2002 – July 1, 2006
Capital One
Trust & Safety ML Lead (Engineering & Data Science)
June 1, 2019 – May 1, 2024
Hybrid
Capital One
Senior Machine Learning Scientist
June 1, 2017 – May 1, 2019
Hybrid
Cerner Corporation
Senior Software Engineer
April 1, 2013 – May 1, 2017
On-site
Cerner Corporation
Software Engineer
January 1, 2010 – March 1, 2013
On-site
Kansas State University
Graduate Research Assistant, Machine Learning & Data Science Lab
August 1, 2007 – December 1, 2009
Manhattan KS · On-site
AWS Certified Cloud Practitioner
Amazon Web Services (AWS)
June 24, 2026 – Present
CS229 Machine Learning (non-credit)
Stanford University
June 24, 2026 – Present
Algorithms: Design and Analysis 1 (Stanford)
Coursera
June 24, 2026 – Present
Learning from Data (Caltech)
edX
June 24, 2026 – Present
Cultural Fit Analysis
The candidate has a diverse background spanning academic research, traditional software engineering, and advanced machine learning/data science roles in large enterprises like Capital One and Cerner. This breadth of experience suggests adaptability and the ability to thrive in different organizational cultures. The progression from Software Engineer to ML Lead demonstrates a growth mindset and ambition. The focus on delivering business value (e.g., fraud savings) aligns with results-oriented cultures.
Soft Skills & Operational Fit
The candidate's experience descriptions highlight leadership in ML R&D, collaboration with multiple stakeholders (business, engineering, model risk), and presentation of lessons learned. This suggests strong communication, collaboration, and problem-solving skills, which are crucial for senior roles. The 'Cerner All-Star' award indicates a strong work ethic and client satisfaction focus.