For hiring teams and technical reviewers
A focused route through the evidence.
Choose a role to review three relevant case studies first. Each result links back to its published methodology, source code and evaluation boundaries. This is a reading guide, not a replacement for the full technical documentation.
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The closest role fit
Data Scientist
Evaluated machine-learning workflows: validation design, calibration, ranking quality, and the review queues a model actually feeds.
Three projects to inspect first
01 · Case study
Fraud Risk Intelligence
An end-to-end fraud analytics platform built on the IEEE-CIS dataset, combining BigQuery, dbt, LightGBM, FastAPI, and a live executive dashboard.
Documented result
590,540 transactions
Limitation / inspection
Categorical columns are passed to LightGBM as integer codes numbered by order of first appearance, so the trees split on an arbitrary category ordering; the repository documents this as a…
02 · Case study
MSCapital Market Forecasting
A production-shaped forecasting system over 804.5M rows of market microstructure, built on a 16 GB laptop, where the main result is an investigation into why the leaderboard disagreed with the…
Documented result
804.5M raw rows reduced to 292 features, 264 distinct
Limitation / inspection
The sequence-order hypothesis is stated at the width its experiment supports. Eighteen hand-built path statistics were tested and bought +0.0006, but the novel ones score 0.0057 standing…
03 · Case study
GS Quant Risk Analytics
A value-at-risk and expected-shortfall application built on a fork of Goldman Sachs' open-source gs-quant library: it measures the risk of a series or a portfolio and then tests whether the VaR model…
Documented result
VaR backtested with Kupiec, Christoffersen and Basel traffic light
Limitation / inspection
Daily closes only, no intraday or real-time prices. The demo runs on simulated GARCH(1,1) data with Student-t innovations, precomputed by the Python code. The deployment files have not been…
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