Usual Machine Learning conceptual questions
Sr Data Scientist Interview Questions
3,390 sr data scientist interview questions shared by candidates
Difference between RAG and Agentic RAG How to evaluate and improve the performance of a deployed model/agent
Round 1: Breadth Assessment This round evaluated the width of my knowledge across the Data Science spectrum. The structure was: Personal introduction Project walkthrough (one detailed project explanation) Technical questions spanning: Machine Learning: Data preprocessing and model evaluation Deep Learning: Optimizers and Gradient Descent Generative AI: RAG (Retrieval-Augmented Generation) and LLMs Coding problems: Printing series patterns and list/dictionary comprehension Difficulty level: Easy to moderate. Round 2: Deep Dive Technical Round This round went significantly deeper into specialized topics: Sentence transformers and their applications Benchmarking and evaluation methodologies RAG architecture and implementation Evaluation frameworks (RAGAs, DSPy) Transformer architecture fundamentals Advanced concepts: Training different word embeddings, contextual awareness, positional encoding
From bedth to depth to hands on
How do you keep yourself updated with current market trends.
What is your Salary expectations
What useful library did you use in python ?(other than Pandas and Numpy)
Basic ML Regularization Over fitting, Under fitting, XGBOOST, RF differences. boosting and bagging etc.
it was a take home project about defining metrics
HackerRank coding - 4 super lengthy question to be done in 90 mins. 1 question of sql and rest 3 on python. Technical interview: mostly project related discussions and few theoretical questions Director interview: All non-tech questions based on behavior, culture etc.
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