Data Scientist, Machine Learning
Middesk
full-time
Posted on:
Location Type: Hybrid
Location: San Francisco • California • United States
Visit company websiteExplore more
Salary
💰 $160,000 - $230,000 per year
About the role
- Build risk & fraud ML applications: Deliver production ML models in fraud, trust & safety, KYB, and compliance domains, with measurable impact on customer workflows.
- Tackle hard data problems: Work on classification problems with extreme class imbalance, sparse signals, and “cold start” label challenges.
- Innovate in feature engineering & labeling: Use graph-based techniques, weak supervision, LLMs, and AI agents to improve signal extraction and automate labeling process.
- Establish ML infrastructure foundations: Partner with platform engineering team to design feature services, model training pipeline, model serving standards, and orchestration to scale multiple ML use cases.
Requirements
- 7+ years applied ML experience, with direct impact in risk, fraud, trust & safety, compliance, or adjacent high-stakes domains.
- Proven track record of shipping ML models from research to production in external-facing products.
- Expertise in classification with real-world challenges, for example: imbalanced labels, sparse signals, cold start, and production version management.
- Hands-on ML infrastructure experience: feature stores, model management, ML training/serving pipelines.
- Comfort as a senior IC: setting technical direction, mentoring peers, and establishing best practices.
Benefits
- Offers Equity 📊 Check your resume score for this job Improve your chances of getting an interview by checking your resume score before you apply. Check Resume Score
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills & Tools
machine learningfeature engineeringclassificationgraph-based techniquesweak supervisionlarge language modelsmodel training pipelinemodel servingsignal extractionlabeling automation
Soft Skills
mentoringsetting technical directionestablishing best practices