Open to Opportunities

Data Scientist &
Credit Strategy Professional.

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.

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Core Competencies

Technical Stack

Python SQL Databricks PySpark VS Code

Data Science

Propensity Modeling Predictive Analytics Feature Engineering A/B Testing

Credit & Strategy

Portfolio Optimization Risk vs Reward Model Governance Product Cross-selling

Leadership

Executive Presentations Stakeholder Alignment Cross-Team Execution Technical Mentorship

Strategic Projects

Case studies demonstrating my ability to navigate complex enterprise data systems, engineer predictive features, and collaborate across teams to optimize business outcomes.

Supervised ML / Survival Analysis

Fintech Probability of Default Engine

The Challenge: Legacy binary classification models only predict if a customer will default, failing to forecast exactly when the business will incur losses.

The Solution: Engineered an end-to-end AWS cloud pipeline using Python and Boto3. Developed a Cox Proportional Hazards model to calculate dynamic Hazard Ratios and 12-month survival curves.

The Impact: Successfully batch-scored 10,000+ unseen loans, exporting predictive timelines to an Amazon S3 Data Lake. Quantified the accelerating effect of Debt-to-Income ratios on default timelines.

View Code Repository
AWS S3 CoxPH Model Boto3
Unsupervised ML / Clustering

Customer Segmentation & Acquisition Engine

The Challenge: A "spray and pray" digital marketing strategy led to high Customer Acquisition Costs (CAC) and low conversion rates across diverse demographic funnels.

The Solution: Built a data generation engine to simulate 100k+ realistic inbound leads. Designed a PySpark pipeline utilizing VectorAssemblers and StandardScaler (Z-Score Normalization) to feed into a K-Means Clustering algorithm.

The Impact: Achieved an elite Silhouette Score of 0.77, discovering three distinct "hidden personas." Enabled dynamic routing to specific business workflows, driving a simulated 3x lift in Click-Through Rates.

View Code Repository
Databricks PySpark K-Means Feature Scaling
Supervised ML / Classification

Strategic Retention Engine

The Challenge: Reactive call-center retention efforts led to high policyholder churn rates and bloated "Cost-to-Serve" operational metrics for enterprise insurance clients.

The Solution: Architected a Databricks pipeline leveraging PySpark and XGBoost to predict customer churn probability based on historical interaction frequency, billing friction, and policy metadata.

The Impact: Enabled proactive intervention strategies by identifying at-risk accounts before they canceled. Translated operational efficiency into a measurable reduction in Cost-to-Serve and improved Customer Lifetime Value (LTV).

View Code Repository
Databricks PySpark XGBoost Classification
Supervised ML / Regression

Predictive Liability & Risk Engine

The Challenge: Traditional actuarial methods rely on broad averages, leading to over-reserving (trapped capital) or under-reserving (P&L shocks) for liability claims.

The Solution: Architected an end-to-end PySpark ML pipeline on Databricks. Engineered a Gradient Boosted Tree model that captures non-linear risk interactions, outperforming standard GLMs, and tracked the deployment via MLflow Unity Catalog.

The Impact: Successfully segmented risk, demonstrating via decile analysis that the model accurately isolates routine claims ($3k) from severe shock losses ($35k+), enabling precise capital allocation.

View Code Repository
Databricks PySpark GBT MLflow MLOps