Applied Scientist Ii

Coalition

Canada
Base: $115,300.00pyear - $144,100.00pyear (ab, bc,...
On-site
Build and advance ml and genai models
End-to-end ml project execution
Design and implement ml pipelines
Coalition is seeking an Applied Scientist II to enhance their machine learning and GenAI models that support underwriting decisions in cyber insurance. The ideal candidate will have robust technical skills in ML and data analysis, alongside experience in insurance, and will work collaboratively with various teams to develop effective decision-making systems

Job Summary

  • Build and improve machine learning and GenAI models that power underwriting decisions and risk selection.
  • Drive and execute ML projects end-to-end, from problem framing to model monitoring.
  • Collaborate closely with underwriters, product managers, and engineers to design robust pipelines and ship impactful models.

Matching Summary

Match Score: 85

Coalition is seeking an Applied Scientist II to enhance their machine learning and GenAI models that support underwriting decisions in cyber insurance. The ideal candidate will have robust technical skills in ML and data analysis, alongside experience in insurance, and will work collaboratively with various teams to develop effective decision-making systems.

Salary

Base: $115,300.00/year - $144,100.00/year (AB, BC, ON); $103,800.00/year - $129,690.00/year (Other locations); Bonus/Equity: Not specified; Benefits: Not specified

Skills & Requirements

Must-have

  • Build and advance ML and GenAI models
  • End-to-end ML project execution
  • Design and implement ML pipelines
  • Apply state-of-the-art ML/GenAI workflows
  • Own model quality and robustness
  • Collaborate with cross-functional partners
  • Communicate methods and results clearly

Nice-to-have

  • Mature empathy and best practices
  • Scientific rigor with practical constraints
  • AI in underwriting frontier

Key Requirements

  • Ph.D. or MS in quantitative/computational field or equivalent experience
  • 5+ years of experience developing and deploying ML solutions
  • Practical experience with supervised/unsupervised learning
  • Expertise in statistical analysis methods
  • Strong proficiency in Python and ML libraries
  • Experience with experiment design and evaluation
  • Comfortable in ambiguous problem spaces
  • Exceptional communication skills
  • Minimum 1+ year in insurance underwriting modeling

Work Rights

Not specified

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