New York, New York, United States
Data scientist and financial crimes technologist building ML-powered solutions for the world's largest banks. I combine 12+ years of hands-on consulting with modern data science to solve complex problems in fraud detection, AML compliance, and risk analytics. What I'm building now: • REST API for geographic risk analysis of customer activity (address, phone, IP, branch proximity, counterparty data) • Supervised ML model predicting NAICS codes from company names/addresses using NLP and advanced feature engineering • ML-driven automated trading system leveraging technical indicators, news sentiment, and fundamental data What I've delivered: • Reduced false positive AML alerts by 15% at a foreign bank under regulatory scrutiny • Cut 3,000+ fraud strategy rules by 60% through automated rule logic parsing • Built 10 custom AML rules when out-of-the-box models couldn't meet complex requirements • Led data mapping efforts with ~80% fewer defects than comparable systems • Clustered geographic regions by money laundering/fraud risk using unsupervised ML Technical depth: Python, SQL, AWS (Lambda, API Gateway, SageMaker, DynamoDB, Glue), scikit-learn, pandas, NLP, geopandas, Cassandra, Actimize SAM/CDD/WLF/RCM/IFM. I speak at industry conferences worldwide (London, Paris, Singapore, Toronto, Kuala Lumpur, NYC) and have contributed to the ACAMS sanctions screening specialist program.
Building ML-powered products and consulting for major financial institutions: API & Product Development: • Developing REST API on AWS (Lambda, API Gateway, DynamoDB) for geographic risk scoring • Building supervised ML pipeline for NAICS code prediction using NLP and classification • Deploying automated trading strategies with feature engineering from market data, news, and fundamentals Consulting Highlights: • Led IFM implementation across P2P/Zelle, wires, ACH, checks, and mobile channels • Designed fraud detection rules for faster payment rails (Zelle, RTP) • Developed 10 complex SAM 9 AML query rules and custom DART views • Calibrated TMS reducing false positives by 15% under regulatory enforcement • Reduced 3,000+ fraud rules by 60% via automated parsing • Built analytics detecting 3+ SD payment anomalies by country, bank, and customer type
•Gathered business requirements from many diverse financial institutions to implement a fully integrated Anti-Money Laundering solution which includes, but not limited to: Suspicious Activity Monitoring (SAM), Customer Due Diligence (CDD) and Watch List Filtering (WLF). •Analyzed system output from multiple alert generation cycles after strategically adjusting model parameters and thresholds and applying statistical methods to reduce false positive alerts and increase the organization's compliance investigation efficiency. •Configured the front-end user interface to support multiple users with varying access permissions and internal system workflows. •Managed implementation of Customer Due Diligence solution which required the integration of multiple systems via a web service module and complex custom model and user interface logic to satisfy the client’s requirements. •Conducted client demos and training sessions for new users to the Actimize Risk Case Manager and Designer.
•Managed two automated Over-the-Counter (OTC) derivatives trade reconciliation projects successfully through all phases of the Software Development Life-Cycle (SDLC). These projects had a direct impact on the following: Credit Risk Limit Monitoring, Credit Exposure Reporting, Collateral Management, BASEL and Credit Valuation Adjustment. •Designed custom stored procedures to reconcile (OTC) derivatives trades from more than 10 distinct trading systems which validated the data flowing into the BASEL Current Exposure Method (CEM) & Internal Modeling Method (IMM) models were accurate within a given confidence interval. •Financial instruments in scope for reconciliation projects included, but were not limited to: FX Spots, FX Forwards, FX Options, FX Exotics, Interest Rate Swaps, Interest Rate Caps & Floors, Swaptions, Repos, Fixed Income Forwards, Debt Options, Convertible Bond Options, Equity Options, Futures, Commodities, Credit Derivatives, Complex Credit Default Swaps. •Discovered inaccurate notional values for amortizing Interest Rate Swaps and drove initiatives across multiple interested parties to change the data feed requirements for specific trading systems, which had a significant impact on capital reserve requirement calculations.
•Managed aspects of Actimize implementation for a variety of major financial institutions including International Investment Banks, US Banks and US Brokerage Firms by acting as an intermediary between the business and technology groups. Scope included equity, fixed income, and futures, Anti-Money Laundering, Market Abuse and Trade Blotters. •Conducted data analysis on source system data residing in an array of systems, including but not limited to SQL Server, Oracle, Sybase, and flat file structures. •Constructed a customized Trade Surveillance model, which included defining the detection scenario, required data elements, formulas, detection flow and scoring. •Developed customized test plans to ensure proper functionality of the Actimize Risk Case that increased the success rate of user acceptance prior to going into production.