Senior Manager, Machine Learning (Data Operations)

Coalition

Confirmed live yesterday High trust
Remote

Quick summary

Work type
Remote
Location
Canada
Posted
55 days ago
Freshness
Confirmed live yesterday

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How this pay compares to similar roles

Similar $210k
$164k most similar roles pay here $257k

This listing doesn't post a salary. Most similar roles pay $172,987–$246,150.

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About Coalition

Coalition is a cyber insurance and security company that combines comprehensive cyber insurance coverage with active risk management tools to help organizations prevent and respond to cyber incidents. Industry: Cyber Insurance & Risk Management

Coalition currently has 21 open roles on FindRole.

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TL;DR · Senior Manager, Machine Learning (Data Operations)

Senior Manager, Machine Learning (Data Operations) joins the product team to ensure high-quality training data for machine learning models. This role serves as the primary point of accountability for labeling quality and methodology, where you will design annotation tasks, define quality frameworks, and manage the Label Studio platform. You will collaborate with ML engineers and product managers to translate model requirements into structured tasks while overseeing outsourced vendors to meet accuracy and throughput targets. Key responsibilities include establishing gold standard datasets, managing inter-annotator agreement, and tracking operational metrics like cost per label. The ideal candidate possesses expertise in annotation quality frameworks, experience with tools like Label Studio or Scale AI, and proficiency in Python and SQL. This role addresses the critical challenge of ensuring labeled data is accurate, consistent, and fit for purpose to power production models.

What you'll do

  • Define annotation guidelines, taxonomies, and edge-case protocols for all labeling programs.
  • Establish gold standard datasets and inter-annotator agreement targets to ensure data quality.
  • Manage the Label Studio platform by defining project configurations and integration requirements with ML infrastructure.
  • Translate machine learning team requirements into structured, unambiguous labeling tasks.
  • Source, onboard, and manage external vendors to meet specific accuracy and throughput targets.
  • Track operational metrics such as label accuracy, cost per label, and turnaround time to drive improvements.
  • Identify opportunities for active learning and model-assisted labeling to reduce costs without sacrificing quality.

What we're looking for

  • 5+ years of experience in ML data operations, data labeling, or a related field like ML engineering or data science.
  • Deep understanding of annotation quality frameworks including IAA, consensus labeling, gold standard evaluation, and error taxonomy.
  • Direct experience managing labeling platforms such as Label Studio, Scale AI, Labelbox, or Prodigy.
  • Proven track record of managing outsourced labeling vendors or BPOs for ML data production.
  • Familiarity with common ML labeling tasks like text classification, NER, document extraction, and intent detection.
  • Proficiency in Python and SQL to support labeling workflows and quality measurement.

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