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Preparing For a Data Science Interview? Do These Things

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According to the LinkedIn 2017 U.S. Emerging Jobs Report, data scientist positions grew 6.5 times in the last five years. So, aspiring candidates with proper training will have multiple vacancies to choose from when seeking out a data science position. All that stands in your way is the interview. Yes, interviews are extremely nerve-wracking, but more so in the case of data scientist positions as only research and preparation will help you ace the big day. But in data science, being an application-based discipline, the expectations vary considerably from one industry to another.


109 Commonly Asked Data Science Interview Questions

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Preparing for an interview is not easy – naturally there is a large amount of uncertainty regarding the data science interview questions you will be asked. No matter how much work experience or technical skill you have, an interviewer can throw you off with a set of questions that you didn't expect. For a data science interview, an interviewer will ask questions spanning a wide range of topics, requiring strong technical knowledge and communication skills from the part of the interviewee. Your statistics, programming, and data modeling skills will be put to the test through a variety of questions and question styles – intentionally designed to keep you on your feet and force you to demonstrate how you operate under pressure. Preparation is a major key to success when in pursuit of a career in data science.


Your ultimate guide to preparing for a tech job interview

ZDNet

So prepare answers to these questions in advance. When practicing answers, make sure to include specific examples to back up your claims. Consider in advance how to frame your experience and expertise. The interviewer has your resume and cover letter. Instead of repeating bullet points from your resume, expand on your accomplishments to give the interviewer examples of your work. Pick out a few of your top achievements and consider your work contributions. Tie these examples into the key phrases you found in the job description.


Lyft Designs the Machine Learning Software Engineering Interview

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Lyft's mission is to improve people's lives with the world's best transportation and it'll be a slow slog to get there with dispatchers manually matching riders with drivers. We need automated decision making, and we need to scale it in a way that optimizes both the user experience and the market efficiency. Complementing our Science roles, an engineer with a knack for practical machine learning and an eye for business impact can help independently build and productionize models that power product experiences that make for an enjoyable commute. A year and a half ago when we began scouting for this type of machine learning-savvy engineer --something we now call the machine learning Software Engineer (ML SWE) -- it wasn't something we knew much about. We looked at other companies' equivalent roles but they weren't exactly contextualized to Lyft's business setting. This need motivated an entirely new role that we set up and started hiring for. Most companies are open about the expectations for the role being interviewed for, the interview process, and preparation tips.


How Lyft designs the Machine Learning Software Engineering interview - WebSystemer.no

#artificialintelligence

Lyft's mission is to improve people's lives with the world's best transportation and it'll be a slow slog to get there with dispatchers manually matching riders with drivers. We need automated decision making, and we need to scale it in a way that optimizes both the user experience and the market efficiency. Complementing our Science roles, an engineer with a knack for practical machine learning and an eye for business impact can help independently build and productionize models that power product experiences that make for an enjoyable commute. A year and a half ago when we began scouting for this type of machine learning-savvy engineer --something we now call the machine learning Software Engineer (ML SWE) -- it wasn't something we knew much about. We looked at other companies' equivalent roles but they weren't exactly contextualized to Lyft's business setting. This need motivated an entirely new role that we set up and started hiring for.