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AI Maintenance Window Risk Scorer

Chase Pham

Full-time · Job · New_York

About the role

We're building the tool that tells ops teams which planned maintenance windows are actually risky before they happen. Our problem is that half of our client incidents trace back to routine maintenance that looked safe on paper but hit an undocumented dependency. You'll build the scoring model that flags high-risk maintenance windows based on service dependency graphs and past incident history. Fast-moving team, big problem, we need someone who ships working code over polished slide decks. You'll sit with our on-call engineers on-site so you can see firsthand which signals actually predict trouble.

Responsibilities

  • Build risk scoring models for planned maintenance windows
  • Mine dependency graphs and incident history for signal
  • Work on-site with on-call engineers to validate signals
  • Ship the scorer into the maintenance scheduling workflow
  • Iterate on the model as new incident data comes in

Requirements

  • 1-2 years in data science, ops, or SRE-adjacent roles
  • Basic Python and SQL skills
  • Interest in incident response and reliability engineering
  • Comfortable working on-site with the ops team
  • Familiarity with workflow automation tools a plus

Benefits

  • On-site role with relocation assistance available
  • Health insurance
  • Mentorship from senior SRE staff
  • Clear path to mid-level promotion
  • Free lunch on office days

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Categories

Automation & AI Workflows MLOps & AI Infrastructure

Skills

python sql scikit-learn airflow statistics

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Job Type

Full-time

Level

Junior

Language

English

Salary Range

Under $60k / year

AI Expertise

Automation & AI Workflows MLOps & AI Infrastructure

Skills

python sql scikit-learn airflow statistics

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