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AI Delivery Time Window Prediction Engineer

Miles Andersen

Full-time · Job · Paris

About the role

You'll be the third data scientist on our logistics forecasting pod, working next to the fleet routing lead. We already have solid routing and warehouse slotting optimization — packages generally get where they need to go efficiently once they're in a truck. What's missing is a tighter prediction on delivery time windows themselves: customers get a four-hour window today, and support tickets spike whenever a driver runs early or late against that estimate. Nobody currently models why those windows miss as often as they do. Your role is to build a delivery time window prediction model that accounts for route, traffic, and driver-specific patterns, tightening the window we can confidently promise to a customer at checkout.

Responsibilities

  • Build delivery time window prediction models using route and driver-level data
  • Reduce the frequency of missed delivery windows flagged by support
  • Partner with the fleet routing lead to incorporate live routing signals
  • Backtest window predictions against historical delivery outcomes
  • Present window accuracy improvements in the logistics forecasting pod review

Requirements

  • Strong Python and pandas skills for large-scale delivery data
  • Experience with scikit-learn or comparable modeling libraries
  • Comfortable with SQL for querying route and delivery event data
  • Familiarity with Spark for processing high-volume delivery event streams

Benefits

  • Remote-first with quarterly pod syncs
  • Health, dental, and vision insurance
  • Stock options with standard vesting
  • Clear ownership within a small forecasting pod
  • Direct visibility into customer-facing delivery accuracy metrics

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Categories

data science & analytics

Skills

python pandas scikit-learn sql statistics

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Job Type

Full-time

Level

Senior

Language

English

Salary Range

$130k – $160k / year

AI Expertise

data science & analytics

Skills

python pandas scikit-learn sql statistics spark

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