Ho presentato la mia candidatura online. La procedura ha richiesto 2 mesi. Ho sostenuto un colloquio presso Apple (Seattle, WA) nel mese di dic 2021
Colloquio
manager screen > tech screen > 4 tech sessions at remote "onsite"
Manager screen: asked about resume, asked brain teaser, asked old school interview questions.
Tech screen: 1 python 3 sql question in 1 hr
On-site: intermediate to advanced SQL, hard python seemed like better for software engineer, lots of hypothetical questions about how you handle different scenarios (disagreement with coworker), hypothetical about how to make spam metric with unlimited data ,presentation
People were extremely condescending and rude, and it seemed like upper management did not trust first-line management to make any decisions. Very waterfall seemingly and so secretive you can't really get a good idea about the work you will be doing or the team culture. All fanboys, any constructive criticism will be frowned upon.
Domande di colloquio [1]
Domanda 1
Name your top 3 skills, what sets you apart form others, what is your biggest regret? SQL build this metric, ok now only look at first row for every group, python sorting function, python search function.
Ho presentato la mia candidatura online. Ho sostenuto un colloquio presso Apple
Colloquio
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.
Ho sostenuto un colloquio presso Apple (Culver City, CA)
Colloquio
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.
Domande di colloquio [1]
Domanda 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.