Data Science has become one of the most sought-after careers in today’s job market, particularly in top notch tech companies like Amazon, Meta (Facebook), Google, and Netflix. However, the skills and qualifications required for a data scientist role can vary greatly between companies. In this blog post, we’ve analyzed over 400 data scientist role-specific questions reported on Fishbowl and Glassdoor to give you a clear understanding of the specific skills and qualifications required by these top companies. With this information, you’ll be better equipped to land your dream data scientist role at the company of your choice.
Cracking the Data Scientist Interview Structure: A Guide to the HR Screening, Technical, and Onsite Interviews of Top Notch Tech Companies
Starting with the HR screening followed by technical and onsite interviews, the entire process on an average takes from 2 weeks to 2 months with each round being of different duration as mentioned in the table
Expected skillet can be generalised in coding skill, behavioural analysis and quantitative analysis for better understanding.
Expected Question Types in Data Scientist Interviews
Apparently, coding consists of different types of questions with distinct abilities needed; however, the prominent subcategories to be expected in programming are- SQL, modeling, data structures and algorithms where statistical coding questions, which are likely to be asked in Google. The dissection of questions from most relevant categories and their expected weightage for Amazon, Google and Meta is illustrated as:
Quantitative analysis, specifically statistics, forms the core of Machine Learning algorithms. Data Scientists use statistics and probability to analyze and gain deeper insights from complex datasets. A/B testing, p-value, Z-test, and hypothesis testing are some of the frequently asked topics in data science interviews for top notch tech companies like Amazon, Google, Meta, and Netflix.
Encountering Machine Learning related questions is common in Google and Amazon. As per the team role, candidates may also be asked about building robust algorithms, feature selection, system design, L1 vs L2 regularization, bagging, boosting, and ML modeling.
In addition to technical and statistical knowledge, the data scientist role also involves driving projects and making business decisions. As such, questions in the form of case scenarios or hypothetical situations may be asked to test a candidate’s product sense. Meta typically asks data and metric-related questions, while Google generally asks about product performance and service-related questions using statistical measures like A/B testing.
At Netflix, SQL, A/B testing, and statistics-related questions can be expected, as well as questions about analytics and product metrics. The specific requirements for each team role may vary, but data science forms the backbone of Netflix’s efficient content recommendation system. Python and Machine Learning-related questions may also be included in the interview process.
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Authored by: Vrinda Tandon
Edited by: Amita Kapoor