Track record
Auditable Predictive/Prescriptive systems, built for enterprise.
Pricing, forecasting, recommendation, and planning systems where latency, reliability, and auditability are non-negotiable.
Production forecasting and demand planning
Pricing, recommendation, and operational decisioning
Technical leadership, hiring, and mentorship
Model deployment and explainability for human review
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Scaled forecasting and operational intelligence across a fast-changing fulfillment network.
- ML platform owner on Databricks / Azure ML Foundry across forecasting, ETA prediction (quantile regression, conformal calibration), classification, outlier detection, and NLP.
- Built ETA prediction (100M+ orders/year) and demand forecasting (1M+ timeseries, weekly) end-to-end.
- Layered in anomaly detection, promotion adjustments, and stock-out corrections to keep forecasts honest under real operating noise.
- Mentored scientists and engineers; translated technical work for product and operations stakeholders.
Owned real-time pricing systems where quality and uptime had direct financial consequences.
- Owner of the ML training and reporting pipelines powering BIN, Market Cost, Market Rate
- Led automated tender acceptance and a baseline market-cost service that matched ensemble accuracy on standard orders using only five shipment inputs.
- Built REST services on AKS with batch endpoints and explainability features for human review.
- Interviewed, hired, and mentored as the team grew.
Delivered pricing and recommendation models that were useful in production, not just accurate offline.
- Deployed pricing models using gradient boosting, quantile regression, and ensembles.
- Tuned recommendation systems for freight matching and demonstrated measurable lift in production.
- Shipped explainability endpoints that translated model behavior into human-readable reasons.
Started in forecasting and experimentation while learning how data products succeed inside real organizations.
- Built enterprise forecasts using ARIMA and ETS across business accounts.
- Automated freight matching workflows in R and Python alongside DBAs, scientists, and engineers.
- Designed and ran A/B tests for recommendation approaches in a live matching application.
Applied predictive modeling in a noisy domain where positive outcomes were rare and precision mattered.
- Led model development across debt portfolios in healthcare, utilities, and other domains.
- Built classification and regression models to estimate payment likelihood and expected value in highly imbalanced data.
- Deployed a scoring workflow for incoming placements and tuned collection strategy from the results.
