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Manager, Machine Learning

2 weeks, 2 days ago
United States
Full-time
Lead
Machine Learning Engineer
Data Science and Analytics
AWS Feature Engineering Machine Learning Python PyTorch SageMaker Scikit-learn SQL XGBoost
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Extend

Extend

Extend is a technology company that offers powerful product and shipping protection solutions to merchants, helping them generate revenue and enhance customer loyalty. Their modern and cost-effective services provide a win-win situation for both busine...

Air Freight & Logistics
251-1K
Founded 2019
View All Jobs 13

Description

  • Lead a team of ML data scientists on the Fraud and ML team.
  • Own the full machine learning model lifecycle, including requirements, experimentation, development, evaluation, and documentation.
  • Translate business problems into well-framed machine learning solutions and determine where ML adds value.
  • Design and maintain feature engineering pipelines for model development.
  • Drive experiment design and ensure statistical rigor before and after model launch.
  • Monitor production model performance, data drift, and retraining needs over time.
  • Partner with Product, Engineering, and Fraud Intelligence teams to integrate and improve machine learning systems.
  • Perform design and code reviews to raise technical quality.
  • Hire, mentor, and coach data scientists.
  • Collaborate across teams to foster a culture of learning and ownership.

Requirements

  • 6+ years of experience building and deploying machine learning systems into production.
  • 2+ years of experience mentoring and managing ML teams.
  • Strong proficiency in Python and SQL.
  • Strong understanding of ML fundamentals, including model selection, evaluation methodology, feature engineering, and common failure modes.
  • Hands-on experience with PyTorch, scikit-learn, and XGBoost or similar gradient boosting frameworks.
  • Strong people leadership skills with the ability to develop ML talent.
  • Excellent stakeholder management and cross-functional collaboration skills.
  • Empathy and humility.
  • Experience building fraud detection or risk assessment systems (preferred).
  • Experience with cloud ML platforms, particularly AWS such as SageMaker (preferred).
  • Experience with graph data and graph-based models such as PyTorch Geometric (preferred).
  • Experience with model monitoring and observability tooling such as Arize (preferred).

Benefits

  • Competitive salary based on experience, with an estimated pay range of $180,000-$210,000 per year.
  • Full medical, dental, and vision benefits.
  • Stock in an early-stage startup growing quickly.
  • Generous, flexible paid time off policy.
  • 401(k) with financial guidance from Morgan Stanley.
  • Collaborative and supportive team environment with diverse backgrounds.

Interested in this position?

Apply directly on the company website

Apply Now

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