Data_Scientist with Core ML (Onsite interview in San jose,CA)

Bachelor’s or Master’s in CS, Statistics, Mathematics or a related quantitative discipline; PhD a plus.
• 8+ years applied ML / data science with a verifiable record of models sustained in production.
• Strong mathematical foundations — linear algebra, probability, statistics, optimization.
• Demonstrated depth in feature engineering and model architecture design, explainable on a
whiteboard for models personally built.
• Proven end-to-end pipeline ownership, not notebook-stage experimentation.

• Expert Python and SQL; pandas, NumPy, scikit-learn and PyTorch or TensorFlow.
• Distributed processing (Spark/PySpark, Hadoop, BigQuery) and a cloud ML platform (SageMaker,
Azure ML or Vertex AI).
• MLOps working knowledge — MLflow, Docker, Kubernetes, CI/CD, model monitoring.

• Production model observability and drift management — threshold definition, automated alerting, kill-
switches and retraining/rollback triggers.

• Experience designing model routing or fallback logic across multiple models, including the validation
applied when a switch occurs.