J'ai postulé en ligne. J'ai passé un entretien chez Apple (Cupertino, CA) en juin 2022
Entretien
The process was simple and the questions were Easy to Medium level. I went only till the Tech Screening.
I applied online and the HR contacted me.
The role was focused on data analytics and not on ML or statistics at all. Not sure why they gave it DS title.
Questions d'entretien [1]
Question 1
SQL:
Find rows starting with letter 'H'
Python:
* Create a DataFrame
* Return first names from a column (use str.split function)
Tableau:
* Context Filters (also other types of filters and uses)
* Dealing with Nan and null values.
* What to do if Tableau visualization loading is slow?
Product:
Another person is sitting next to me in a room performing tests and it interferes with my Mac power and causes power fluctuations. How to confirm if that person's tests are causing the power fluctuations?
You have access to a report of when the tests are run.
General:
Data Analysis workflow
Dealing with NaN's and null values
Worked on Splunk?
Worked on Mongo DB?
J'ai postulé en ligne. J'ai passé un entretien chez Apple
Entretien
First Round: 2 questions on SQL (hard) and Scenario based question was given. You have to provide a detailed answer on what kind of framework you would build on the scenario.
J'ai passé un entretien chez Apple (Culver City, CA)
Entretien
The interview process for the Data Scientist role at Apple consisted of three rounds
First round: screen with a recruiter
Second round: screen with a hiring manager
Third round: a virtual on-site with 3 interviews
The interview process consisted of multiple rounds, including an initial recruiter conversation followed by several virtual interviews with team members. The interviews focused on my background, past data science projects, machine learning fundamentals, statistical thinking, and how I approach business problems with data. There was also discussion around cross-functional collaboration, communication, and how I would translate technical insights into actionable recommendations. Overall, the process was rigorous and comprehensive, with a strong emphasis on both technical depth and practical problem solving.
Questions d'entretien [1]
Question 1
Tell me about one of your data science projects and explain the business problem, your modeling approach, the metrics you used, and how you communicated the results to stakeholders.