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Data Scientist Interview Questions

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Data Scientist Interview Questions

In un colloquio per Data scientist, ti verranno poste domande volte a verificare le tue capacità di data modeling, risoluzione di problemi e programmazione. Preparati a rispondere a domande di carattere generale che valutano la tua conoscenza della statistica e della scienza dei dati. Dovresti inoltre prepararti a rispondere a domande aperte mirate a testare la tua creatività, le tue doti comunicative e la tua formazione nella programmazione e modellazione dei dati.

Domande tipiche dei colloqui per Data scientist e come rispondere

Question 1

Domanda 1: Quali tecniche di data modeling preferisci e perché?

How to answer
Come rispondere: Trasformare i dati in informazioni comprensibili e fruibili è un aspetto fondamentale del lavoro di Data scientist. Con questa domanda i datori di lavoro vogliono capire il tuo backgruond e valutare le tue capacità di data modeling. Elenca e illustra le tecniche di data modeling che preferisci, includendo vantaggi come semplicità d'uso, flessibilità, ecc.
Question 2

Domanda 2: Come rilevi gli account Instagram fasulli utilizzati per raggirare i consumatori?

How to answer
Come rispondere: Domande come questa permettono ai selezionatori di testare le tue capacità di risolvere i problemi. Quando rispondi a domande aperte di questo tipo, non esitare a chiedere chiarimenti o a usare lavagne per dimostrare le tue abilità nel tracciare diagrammi e usare codici. Condividi il tuo processo di pensiero mentre elabori il problema.
Question 3

Domanda 3: Descrivi quali circostanze richiedono una lista, una tupla o un set in Python.

How to answer
Come rispondere: I selezionatori ti porranno domande come questa per testare le tue abilità di programmazione in Python. Ripassa gli elementi fondamentali di Python, come liste, tuple e set prima del colloquio. Dovrai essere in grado di spiegare quando e come ogni strumento deve essere usato da un Data scientist.

54,301 data scientist interview questions shared by candidates

Which of the 10 MBM Koch values do you identify with most strongly, and why? Tell us about a time you went above and beyond on a particular assignment or job duty. Tell us about a time you disagreed with your supervisor, etc.
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PhD Research Scientist

Interviewed at INVISTA

3.7★
Dec 3, 2017

Which of the 10 MBM Koch values do you identify with most strongly, and why? Tell us about a time you went above and beyond on a particular assignment or job duty. Tell us about a time you disagreed with your supervisor, etc.

Write a social network that runs in memory. There should be four functions. makePost(userID, postID) follow(followerID, followedID) unfollow(followerID, followedID) showFeed(userID) The show feed function should show the posts made by the user, the posts made by the people the user followed and they should be in chronological order.
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Data Scientist - Machine Learning

Interviewed at Citizen (NY)

2.6★
Mar 10, 2025

Write a social network that runs in memory. There should be four functions. makePost(userID, postID) follow(followerID, followedID) unfollow(followerID, followedID) showFeed(userID) The show feed function should show the posts made by the user, the posts made by the people the user followed and they should be in chronological order.

Standard questions about how I tackle business and data problems etc. However, as a data scientist I noticed one potentially red flag: the company is new and fresh in their data science department (although they have a solid data engineering and reporting base), but they want to start straight with GenAI/LLM stuff (Chat-GPT and whatnot). For those unfamiliar: it is pretty much a guaranteed pitfall as people would be using super-complex tools with zero knowledge of their inner workings (think of learning to run before you can walk).
avatar

Data Scientist

Interviewed at Marel

3.8★
Jan 28, 2024

Standard questions about how I tackle business and data problems etc. However, as a data scientist I noticed one potentially red flag: the company is new and fresh in their data science department (although they have a solid data engineering and reporting base), but they want to start straight with GenAI/LLM stuff (Chat-GPT and whatnot). For those unfamiliar: it is pretty much a guaranteed pitfall as people would be using super-complex tools with zero knowledge of their inner workings (think of learning to run before you can walk).

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