Employee Attrition
Identify at-risk employees and improve retention with ML classification.
Business problem
HR teams struggle to spot flight-risk employees early. Reactive exits cost recruiting budget and institutional knowledge.
Heimdall solution
Combine HR features in Lake gold tables and train a classification model in Heimdall ML. People teams prioritize retention outreach on predicted risk scores.
Business impact
Targeted retention programs address at-risk employees before they leave, reducing turnover cost.
How it works
Publish HR gold features
Curate tenure, performance, compensation, and engagement signals in Lake.
Train classification in ML
Build an explainable attrition model without notebooks or AutoML scripts.
Act on risk scores
Export scores or call APIs to integrate with HRIS workflows.
See Heimdall in action
Tell us about your retention analytics goals
Key benefits
Early warning
Flag at-risk employees before exit interviews
Explainable drivers
See which factors correlate with attrition
Governed data
Gold HR tables with clear lineage
No-code ML
HR analysts can iterate models safely
API integration
Scores feed existing HR tools
Privacy-aware workflow
Keep sensitive HR data in your Lake catalog
Related use cases
More scenarios on the use cases hub.