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Wissenschaftler Interview Questions

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Wissenschaftler Interview Questions

Bei einem Vorstellungsgespräch für Wissenschaftler wird von Ihnen Fachwissen und die nötige Erfahrung für die jeweilige Stelle erwartet. Häufig angesprochene Themen sind beispielsweise grundlegende statistische Methoden, Konzepte des maschinellen Lernens und Analyse von Fallstudien. Die befragende Person wird höchstwahrscheinlich auch Ihre Kommunikations- und zwischenmenschlichen Fähigkeiten beurteilen, die für eine effektive Arbeit im Team und das Einwerben von Drittmitteln unabdingbar sind.

Typische Bewerbungsfragen als Wissenschaftler (m/w/d) und wie Sie diese beantworten

Question 1

Frage 1: Was versteht man unter Konzept X? Was sind dessen Annahmen und wie wenden Sie dies an?

How to answer
So beantworten Sie die Frage: Im Grunde wird hierbei eine Lektion aus dem Lehrbuch für ein bestimmtes Konzept des maschinellen Lernens sowie dessen Bedingungen und Anwendungen abgefragt. Vermeiden Sie zu komplizierte Antworten. Geben Sie eine einfache und geradlinige Antwort, die zeigt, dass Sie gut mit dem Konzept vertraut sind.
Question 2

Frage 2: Nennen Sie ein Beispiel für ein Problem, dem Sie in einer früheren Position begegnet sind, und erläutern Sie, wie Sie es behoben haben.

How to answer
So beantworten Sie die Frage: Die befragende Person möchte Ihre Problemlösungskompetenzen in Erfahrung bringen. Wählen Sie überlegt eine schwierige Situation, die Ihre Fähigkeit zur Problemlösung optimal wiedergibt, und erklären Sie, was Sie unternommen haben, um das Problem zu überwinden. Es wäre von Vorteil, wenn das Problem auch für die gewünschte Position relevant ist.
Question 3

Frage 3: Wie würden Sie Drittmittel einwerben?

How to answer
So beantworten Sie die Frage: Falls Sie bereits erfolgreich Drittmittel zur Forschungsförderung eingeworben haben, können Sie die dabei verwendeten Methoden ansprechen. Falls nicht, heben Sie Ihre Fähigkeiten hervor, die bei der Mittelbeschaffung helfen können, wie das Verfassen von erfolgsversprechenden Förderanträgen und effektives Netzwerken.

33,448 wissenschaftler interview questions shared by candidates

Case Interview: the case is the car finance loan. - what are revenues and expenses - given a model that predicts when a customer is good (loan should be approved) or bad (loadn should be decline) find out: 1. the probability that the customer is good given the model predicts good 2. the probability that the customer is bad given the model is good 3. given a pentile graph of # of checked off loans / # of loans what is a better model than the current; what is the best model. Behavioral interview: - tell me about a time that you had to deal with changing objectives in your team/project - tell me about a time that you had to deal with unexpected problems in your project - tell me about a time that you had to persuase somebody Role interview: the case is a report on air company with low percentage of flight on time. Read the report an give an evaluation of it and some reccomendations to your boss. 15 minutes to read the report and remove anything unecessary or spot errors. 20 minutes to present it to your boss. 15 minutes to discuss afterwards from data scientist to data scientist.
avatar

Data Scientist Intern

Interviewed at Capital One

Oct 14, 2016

Case Interview: the case is the car finance loan. - what are revenues and expenses - given a model that predicts when a customer is good (loan should be approved) or bad (loadn should be decline) find out: 1. the probability that the customer is good given the model predicts good 2. the probability that the customer is bad given the model is good 3. given a pentile graph of # of checked off loans / # of loans what is a better model than the current; what is the best model. Behavioral interview: - tell me about a time that you had to deal with changing objectives in your team/project - tell me about a time that you had to deal with unexpected problems in your project - tell me about a time that you had to persuase somebody Role interview: the case is a report on air company with low percentage of flight on time. Read the report an give an evaluation of it and some reccomendations to your boss. 15 minutes to read the report and remove anything unecessary or spot errors. 20 minutes to present it to your boss. 15 minutes to discuss afterwards from data scientist to data scientist.

A set of values given: Assume table in SQL or list of dictionaries if using Python. Basically a row of data contained information: if it is post or it is a comment, row id and some other data. Find distribution of comments. #comments # posts 1 5000 2 6787 .. ..
avatar

Data Scientist

Interviewed at Meta

3.6★
Sep 27, 2017

A set of values given: Assume table in SQL or list of dictionaries if using Python. Basically a row of data contained information: if it is post or it is a comment, row id and some other data. Find distribution of comments. #comments # posts 1 5000 2 6787 .. ..

R4: Assume the distribution of children per family is given by: # children 0 | 1 | 2 | 3 | 4 | >=5 p 0.3 | 0.25 | 0.2 | 0.15 | 0.1 | 0 Consider a random girl in the population of children. What's the probability that she has a sister?
avatar

Data Scientist

Interviewed at Google

4.4★
Sep 2, 2021

R4: Assume the distribution of children per family is given by: # children 0 | 1 | 2 | 3 | 4 | >=5 p 0.3 | 0.25 | 0.2 | 0.15 | 0.1 | 0 Consider a random girl in the population of children. What's the probability that she has a sister?

SQL: there is a table of time,post id, action and content. the action can be reported and the content is spam. another table of time,post id, user - of all posts were removed manually the question: What percent of yesterday's content views were on content that has been reported for spam and removed yesterday?
avatar

Data Scientist

Interviewed at Meta

3.6★
Jun 2, 2020

SQL: there is a table of time,post id, action and content. the action can be reported and the content is spam. another table of time,post id, user - of all posts were removed manually the question: What percent of yesterday's content views were on content that has been reported for spam and removed yesterday?

You're about to get on a plane to Seattle. You want to know if you should bring an umbrella. You call 3 random friends of yours who live there and ask each independently if it's raining. Each of your friends has a 2/3 chance of telling you the truth and a 1/3 chance of messing with you by lying. All 3 friends tell you that "Yes" it is raining. What is the probability that it's actually raining in Seattle?
avatar

Data Scientist

Interviewed at Microsoft

4★
Sep 19, 2016

You're about to get on a plane to Seattle. You want to know if you should bring an umbrella. You call 3 random friends of yours who live there and ask each independently if it's raining. Each of your friends has a 2/3 chance of telling you the truth and a 1/3 chance of messing with you by lying. All 3 friends tell you that "Yes" it is raining. What is the probability that it's actually raining in Seattle?

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