Derived from 1 submission for ALL. Format details may vary across different teams or locations.
The interview process for Data Science / ML roles at UBS typically consists of three rounds: two technical rounds followed by an HR round. Candidates are selected based on their performance in project-based discussions and technical problem-solving, with a strong emphasis on Python, Spark, and SQL.
The candidate underwent three rounds of interviews at UBS for a Data Analyst role. They were selected after the second technical round and later received an offer after the HR round. The process included project-based technical discussions, Python, Flask, Spark, and HR-related queries.
A 45-minute project-based interview conducted by a director. The interviewer asked about the candidate's video analytics project and technical questions, followed by some behavioral questions.
A 30-minute technical interview focused on Python, Flask, Spark, and SQL. The interviewer asked about Python code snippets, object-oriented programming in Python, Flask API creation, Spark basics (Parquet, HDFS), and differences between DataFrame and RDD.
Python code snippets for output prediction
Object-oriented programming concepts in Python
Flask server restart avoidance
Creating a basic API in Flask
Spark basics: Parquet format and HDFS
Difference between DataFrame and RDD in Spark
A 30-minute HR round where typical HR questions were asked, including a warning about potential consequences if the candidate left the company after accepting the offer. Salary negotiation also took place.
InterviewDigest