Freddie Mac interview questions

Showing 1–4 of 4 results

• Compute leakage-safe rolling features in pandasMedium

  This question evaluates proficiency in time-series feature engineering, leakage prevention, memory-efficient large-scale data processing, categorical target encoding, exponential weighting, and algorithmic complexity within the Data Manipulation (SQL/Python) domain, emphasizing practical application with pandas at scale.

  Freddie MacData ScientistData Manipulation (SQL/Python)Onsite
• Describe a Machine Learning ProjectMedium

  This question evaluates a candidate's ability to lead end-to-end machine learning projects, emphasizing technical ownership, system design, data strategy, model selection, deployment, and stakeholder management.

  Freddie MacMachine Learning EngineerBehavioral & LeadershipTechnical Screen
• Implement blocked time-series cross-validationMedium

  This question evaluates competence in designing panel-aware time-series cross-validation with embargo handling, grouped blocking, deterministic splitting, scalability analysis, and adversarial unit-test design for issues like duplicate timestamps and entity migration.

  Freddie MacData ScientistCoding & AlgorithmsOnsite
• Improve Trust in a RAG SystemMedium

  This question evaluates system-design competency for trustworthy retrieval-augmented generation, probing skills in retrieval and generation architecture, confidence scoring and calibration, evidence verification and fallback mechanisms, monitoring, privacy and compliance, human feedback integration, and adversarial robustness.

  Freddie MacMachine Learning EngineerML System DesignTechnical Screen