Bridging the gap between advanced predictive analytics, scalable ML architecture, and cross-functional business execution.
Drawing on over a decade of experience across banking, insurance, and tech manufacturing, I specialize in applying cross-industry predictive frameworks to engineer modern credit and growth strategies. With expertise in Python, Databricks, and SQL, I design end-to-end analytical pipelines. Beyond the code, I thrive as a strategic bridge—translating complex data models to secure executive buy-in, mentoring technical teams, and partnering directly with marketing and business units to execute highly targeted campaigns.
Case studies demonstrating my ability to navigate complex enterprise data systems, engineer predictive features, and collaborate across teams to optimize business outcomes.
The Challenge: Traditional credit propensity models rely on static demographic data, missing dynamic financial behaviors.
The Solution: Architected a novel feature engineering framework using transactional data to quantify Share of Wallet and Relationship Depth. Translated these complex technical signals into business strategies for executive stakeholders.
The Impact: Created a scalable blueprint to identify high-value, low-risk clients, partnering directly with Marketing and Business teams to deploy these insights into live Authorized User campaigns.
The Challenge: Disconnected enterprise data science teams building redundant predictive models, leading to governance risks and wasted cloud compute.
The Solution: Conducted an architectural audit of a proposed propensity model build. Discovered an overlapping model and executed a strategic handover, consolidating custom logic into the central team's production pipeline while mentoring junior analysts on architectural standards.
The Impact: Secured executive buy-in to halt redundant development, eliminated data governance risks, and optimized enterprise ML resources across multiple silos.
Overview: An end-to-end machine learning pipeline built to predict loan defaults and output risk tiers for new applicants.
Technical Execution: Executed comprehensive data cleaning, handled class imbalances using SMOTE, and trained an XGBoost/Random Forest classification model within a modern workspace environment.
Business Value: Demonstrates the ability to translate raw tabular data into robust, deployed models that protect portfolio health while approving high-quality applicants.