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Machine Learning Research Engineer Interview Questions

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1,879 machine learning research engineer interview questions shared by candidates

- SQL & Python (Basics) * Basic questions on SQL Joins, Unions, Filtering, String & Date functions, Aggregate functions. * Basic user defined functions, visualizations, pandas functions - SQL (Advanced) * Queries using window functions (Row Number, Rank, Dense Rank), sub-queries in SQL to check approach and logic to solve a problem - Python (Advanced) + ML * user defined functions * Asked to explain one of the projects listed on my resume, few basic ML questions * Exploratory data analysis questions - Apti & HR * questions on Number series and puzzles, basic statistical questions. * HR questions on background, personality check questions, why analytics, why dhiOmics.
avatar

Machine Learning Scientist

Interviewed at dhiOmics Analytics Solutions

3.7★
Sep 1, 2020

- SQL & Python (Basics) * Basic questions on SQL Joins, Unions, Filtering, String & Date functions, Aggregate functions. * Basic user defined functions, visualizations, pandas functions - SQL (Advanced) * Queries using window functions (Row Number, Rank, Dense Rank), sub-queries in SQL to check approach and logic to solve a problem - Python (Advanced) + ML * user defined functions * Asked to explain one of the projects listed on my resume, few basic ML questions * Exploratory data analysis questions - Apti & HR * questions on Number series and puzzles, basic statistical questions. * HR questions on background, personality check questions, why analytics, why dhiOmics.

In SQL questions were more related to window functions, joins, subqueries etc. In python questions were based on dataset filtering and visualization. In ML, questions were more related to concepts behind algorithms.(Linear regression, logistic regression etc)
avatar

Machine Learning Scientist

Interviewed at dhiOmics Analytics Solutions

3.7★
Dec 10, 2020

In SQL questions were more related to window functions, joins, subqueries etc. In python questions were based on dataset filtering and visualization. In ML, questions were more related to concepts behind algorithms.(Linear regression, logistic regression etc)

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