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Crack the Amazon Data Scientist Interviews

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Do you aspire to become a Data Scientist, ML Engineer, Applied Scientist or Research Scientist at Amazon? This guide will provide you comprehensive details about the interview process and preparation tips to help you ace the data interviews at Amazon. I created dataInterview.com to help a candidate such as yourself ace data science interviews and land your dream role at a top company. Make sure to check it out! Before we start, please note that that the exact interview experience at Amazon can vary given the role, team, and interviewer's preference. In general, the details and tips provided should be helpful with your interview prep. As you might already know, Amazon is a conglomerate of multiple businesses from e-commerce (Amazon.com),


Apple Data Science Interview Questions

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Apple Inc. is one of the biggest technology companies in the world that designs, develops, and sells consumer electronics, computer software, and online services. Apple is constantly in need of creative, passionate, and dedicated data scientists that can sit on any number of their teams. From its researched-based artificial intelligence development team at Siri to cloud-base architecture development team at iCloud, Apple has slowly but steadily been building data science teams to handle the avalanche of data accumulated on a daily basis. As with other big tech companies, the role of a data scientist at Apple varies a lot and is dependent on the teams you are assigned to. This means the job will require everything from analytics to machine learning software design to plain engineering.


Data Science Role and Environment at Microsoft

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After being named sexiest job of the 21st Century" by Harvard Business Review, data science has blended the enthusiasm of the overall population. Numerous individuals are fascinated by the job and can't help thinking about how they themselves can become data scientists. There are endless tools, courses, and applications for people to learn data science, however, let's be honest: for somebody new to the field, every one of these options can appear to be a jungle of complex information. Microsoft has been a major player in the data science industry after Azure and it's machine learning tools have been gradually ruling as the biggest service provider in the cloud-computing market. Therefore, Microsoft has been working out its data science team gradually in recent years to get one of the greatest companies hiring for data scientists.


How to Land a Job as a Data Scientist at Amazon?

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Amazon is the world's largest and leading online retailing company. The company's business is continuously growing as it fulfils its customers' demands strategically and effectively as well. Amazon's marketing strategy is something that can help a business – from small to large. Amazon is referred to as one of the top companies for data scientists, offering both handsome salaries and exciting career opportunities. Read on to learn how to get onboard as a data scientist at Amazon.


A Guide to Hiring Data Scientists

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Data science is an emerging field, and roles, as well as qualifications, aren't clear-cut at the moment. Given the murkiness surrounding the field and the potential lack of analytics expertise at companies seeking to hire a data scientist or team of data scientists, the task of building an analytics team or hiring a company's first data scientist can be daunting. However, with a brief overview of data scientist types and example questions to assess each type, hiring managers can provide recruiters with a more tailored profile and better assess candidates on skills likely needed to fill the role. Data scientists typically have skills in 3 main areas: mathematics/statistics/machine learning, coding/software engineering, and expertise in the industry in which they seek employment (see chart below). Most mature data scientists have a strong skills in 2 of these 3 areas, yielding software/math folks (who are typically found in tech companies or production roles), math/domain folks (more of a traditional statistician or scientific researcher), or software/domain (less common but often involved in data pipelines and business intelligence roles).