Career Work
All Projects
25 production ML systems built across 4+ years. Every project shipped to production and owned end-to-end, from research through maintenance.
Revenue Forecasting Platform
Production ML system predicting pipeline and booking outcomes at daily frequency across current and future quarters.
Marketing Mix Modeling Platform
Full-stack marketing attribution and budget optimization platform: from raw channel spend to response curves and scenario planning.
Multi-Touch Attribution System
Probabilistic multi-touch attribution using Markov Chain removal-effect modeling, crediting each channel based on its actual influence on conversion probability across the full customer journey.
Full Pipeline Projection Engine
Central projection system integrating 8+ ML model families into a single daily revenue forecast across four quarter horizons.
Demand Generation Potential Model
Estimates pipeline contribution from opportunities not yet visible in the CRM (the unseen quarter contribution).
Walk-In Pipeline Projection
Regression-based estimate of within-quarter pipeline creation from sources not visible at quarter start.
Account Propensity Model
Dual propensity scoring system assigning Base Fit (long-term ICP fit) and 3-Month (short-term conversion likelihood) scores to every account.
Lead Propensity Model
Scores each lead's conversion likelihood across four quarter horizons (CQ/NQ/NQ+1/NQ+2) using engagement patterns and journey sequences.
Opportunity Propensity Model
Predicts each opportunity's close likelihood per quarter, replacing static CRM probability fields with dynamically scored multi-quarter estimates.
Account Propensity X Month
Monthly-granularity refactor of the Account Propensity model, enabling month-level account prioritization alongside quarterly scores.
Time-Decay Forecast Adjustment
Dynamic adjustment layer that re-weights prior EOQ history by recency, capturing momentum and velocity signals closer to quarter end.
Average-Index Forecast Adjustment
Statistical fallback layer using day-of-quarter index averages when ML model signals are insufficient.
Forecast Explainability & SHAP Logging
Dual-algorithm explainability infrastructure: SHAP for tree models, coefficient contribution for linear models, with BigQuery logging.
Configurable Multi-Model ML Framework
30+ parameter framework for runtime algorithm selection, feature group configuration, and model stacking across the forecasting platform.
Campaign Performance Prediction Engine
Two-stage system predicting campaign pipeline potential before launch and monitoring active campaigns daily.
Revenue Metrics Pipeline Migration
Migrated the revenue metrics pipeline from single-machine pandas to distributed PySpark, resolving critical implementation bugs to unblock daily ML scoring.
Campaign Entity Resolution
Fuzzy matching + BERT embedding system to deduplicate and resolve campaign entities across CRM and marketing platform data sources.
Smart Filter Parser & SQL Generation
Metadata-driven SQL generation system translating user-defined filter configurations into executable BigQuery queries.
Generic Regressor Framework
Reusable ML regression framework standardizing feature engineering, training, validation, and scoring, adopted as the baseline for all subsequent platform models.
Model Metric Dashboard
Internal ML governance dashboard tracking train/test MAPE, MAE, RMSE, and classification metrics across all deployed platform models.
Product Prediction Model
Predicts which products are likely associated with a lead conversion, improving downstream opportunity size estimation.
Opportunity Size Prediction
Predicts booking dollar value for leads and early-stage opportunities, enabling per-record expected value computation.
Customer Segmentation Clustering
Unsupervised clustering model grouping accounts by firmographic and behavioral attributes for GTM targeting.
Booking Prediction Model
Record-level ML system predicting booking likelihood and value, contributing to pipeline and booking projection.
Record Level Likelihood & Win Rate
ML-derived win rate methodology using per-record conversion likelihoods instead of binary counts for smoother, more accurate pipeline reporting.