Interview Scorecard Templates
Structured evaluation templates for AI, ML, and data science roles — with competency frameworks, rating anchors, and a hire/no-hire recommendation section.
AI Engineer Interview Scorecard
A structured scorecard for evaluating AI engineer candidates. Covers LLM proficiency, RAG system design, AI application architecture, practical engineering judgment, and communication clarity. Includes competency definitions and anchor descriptions for each rating level.
Competencies covered
- LLM understanding & prompt engineering
- RAG and retrieval system design
- AI application architecture
- Production engineering judgment
- Communication and explanation quality
ML Engineer Interview Scorecard
A structured scorecard for evaluating ML engineer candidates. Covers ML system design, feature engineering, model evaluation, experiment discipline, and serving infrastructure. Includes a hire/no-hire recommendation section with supporting rationale.
Competencies covered
- ML system design
- Feature engineering & pipelines
- Model evaluation & experimentation
- Production infrastructure & serving
- Operational maturity & debugging
Data Scientist Interview Scorecard
A structured scorecard for evaluating data scientist candidates. Covers statistical rigor, experiment design, applied modeling, data quality judgment, and stakeholder communication. Designed to distinguish between technically strong candidates and those who can also drive business decisions.
Competencies covered
- Statistical reasoning & inference
- Experiment design & A/B testing
- Applied modeling judgment
- Data quality & analytical rigor
- Business communication & influence
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