Dec 2024 - Present
Driver Efficiency Scoring with Double Machine Learning
Built a causal evaluation framework that separated driver behavior from route and weather confounders, producing fair rankings and actionable coaching insights for fleet operations.
Drivers Evaluated
35+
Efficiency Gap Quantified
$0.14 kWh/km (12%)
Savings Opportunity
$10k+/fleet annually
Causal DAG
Problem
Fleet performance comparisons were noisy because route topography, traffic, and weather heavily biased raw efficiency measurements.
Approach
I designed a Double Machine Learning pipeline to isolate behavioral effects from environmental covariates.
- Built treatment and outcome models with cross-fitting to reduce overfitting bias.
- Added mixed-effects modeling so scores remained stable across driver cohorts and repeated trips.
- Introduced matched-pair validation and SHAP diagnostics to confirm directionality and model consistency.
Outcome
The final scorecard highlighted operational inefficiencies with statistically defensible confidence, enabling targeted coaching for throttle smoothness and route-specific behavior.
Why it mattered
This work moved discussions from anecdotal feedback to causal evidence, which improved trust from operations stakeholders and accelerated coaching adoption.