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
$38K+/fleet annually
Fairness Stabilization Trend
Normalized confounder-adjusted scoring performance over release cycles.
As confounder controls improved, ranking consistency increased and recommendations became easier to operationalize.
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.
- Gathered and cleaned telemetry from 55K+ trips, then engineered route and driver features for fair comparison.
- 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 a 12% driver-efficiency gap across 35+ drivers 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 and quantified a $38K+/fleet/year savings opportunity, improving trust from operations stakeholders and accelerating coaching adoption.