Selected Work

Analytical Rigour Met
with Visual Clarity.

A curated selection of data-driven narratives, ranging from high-frequency financial modeling to neural network architectures.

PROJECT 01

Predictive Liquidity Modeling

Implementing a localized stochastic volatility model for real-time asset pricing and risk assessment in high-frequency trading environments.

PythonStochastic Models
PROJECT 02

Neural Attribution Logic

Deconstructing black-box algorithms through Layer-wise Relevance Propagation to ensure ethical data practices.

Deep LearningXAI
PROJECT 03

Consumer Sentiment Mapping

Processing 2M+ data points from social APIs to map regional sentiment shifts regarding sustainable tech.

NLP2M+ Records
PROJECT 04

Supply Chain Optimization

Graph-based analysis of global logistics to identify and mitigate bottleneck risks in tier-2 manufacturing.

Graph TheoryTableau
PROJECT 05

Churn Prediction V2

Reducing customer attrition by 14% through an ensemble XGBoost model integrated into SaaS workflows.

XGBoost–14% Churn

Featured Case Study

The Volatility Index

Decoding supply chain disruptions through high-frequency logistics data and Bayesian structural time-series modeling.

01 / The Challenge

A Fragmented Landscape

Global logistics firms were struggling with a 14% variance in arrival time predictions, leading to millions in annual port fees. The primary hurdle was the "Black Swan" effect—unforeseen regional disruptions that standard linear models failed to capture.

14.2%

Prediction Variance

$2.4M

Annual Latency Cost

02 / The Data

Sources

Real-time AIS Vessel Tracking
Historical Port Congestion Logs
NOAA Meteorological Data

Toolkit

Python PostgreSQL Tableau PyMC3 Pandas

03 / The Methodology

Precision in Process

Anomaly Filtration

Automated scripts to identify and remove AIS transmission gaps greater than 4 hours.

Feature Engineering

Synthesizing 'Port Saturation' metrics by correlating vessel density with berth availability.

Bayesian Modeling

Applying MCMC sampling to account for uncertainty in localized weather-induced delays.

04 / The Insights

Visualizing Convergence

Our analysis revealed that 62% of arrival delays were not caused by oceanic conditions, but by 'Invisible Bottlenecks'—minor local labor shifts identifiable 72 hours in advance via density patterns.

Convergence Visualization — Interactive Chart

05 / The Impact

Strategic Resolution

By integrating the predictive Bayesian model into the fleet's dispatch system, the client achieved an 11% reduction in unplanned port dwell time within the first quarter.

Precision Boost

76% → 91%

Model accuracy increased for trans-pacific routes.

Cost Savings

$1.8M

Estimated annual savings in dynamic route optimization.

Ready to unlock your data's story?

Available for consulting on specialized data architecture and advanced predictive analytics.