I applied online. The process took 3 weeks. I interviewed at Featurespace
Interview
Phonecall with head of Data Science who explained the role to me. Then a coding task and then an on-site interview.
The on-site interview has two parts: a data science part and a computer science part. The data science part includes questions about how to train an ML algorithm and other concepts in ML. The computer science part included an algorithm question and questions about parallel computing.
Interview questions [1]
Question 1
Given an array of integers and a target integer, find all pairs of integers in the array that sum up to the target integer.
I applied online. The process took 3 weeks. I interviewed at Featurespace in Mar 2020
Interview
15 minute takehome Python exercise, followed by a 30 minute phone interview with line manager, followed by a longer takehome Python exercise (a script that processes a CSV file of customer transactions, keeps a running mean and standard deviation of transactions by customer ID, observes large deviations of individual transaction amounts from the running mean and, when such deviations are observed, generates alerts)
Interview questions [1]
Question 1
The phone interview was generally non-technical.
The 2nd takehome exercise task was to write a script that processes a CSV file of customer transactions, keeps a running mean and standard deviation of transactions by customer ID, observes large deviations of individual transaction amounts from the running mean and, when such deviations are observed, generates alerts. This was to be done by implementing just one method, for which they had written a header definition.
The instructions stated certain requirements: scalability, readability and code structure, logging and testing, and how simple it is to run and evaluate the code. Observe that some of these involve a large element of subjectivity. There was no mention of persistence, or function naming conventions.
