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After years of working on your PhD and being in academia, transitioning into a Data Science career can be a culture shock.
It’s important to understand how the roles you are wanting to apply to vary; what industry is it in? What are the main focus of the projects? Who will you be working alongside? What are the end goals the team would be working on achieving? Does your chosen industry have anything to do with your PhD and academic research?
You know who you are. A high-calibre machine learning magician, a well-versed wrangler of data... but you want a bit more from your role. That may be progression, more money or the chance to work on new, more exciting projects, but where do you go from here?
Many companies are looking to increase investment in data science departments and looking for leaders to build out new teams to do this. But before you take the plunge into the C-level, weigh up what this role entails and…Continue
It can be difficult to know what to expect when going for an interview. Data Science interviews will require candidates to answer technical questions, and often take on technical exercises depending on the company and role you’re going for.
But often overlooked is the talk about soft skills, such as communication skills business savvy, creativity and impact your work has had in the past.
Here is a selection of non-technical questions you could get asked to help you prepare for…Continue
Sat across from the interviewer for your dream job, you may start to feel the pressure. A sure-fire way to quash the interview jitters is to prepare as much as possible. Typically, you can segment the types of questions you’ll get asked in a data science interview; things such as statistics, programming and technical ability, business acumen, and culture fit assessment. Studying up on these will help you prepare as best you can.
Here are some examples of what you…Continue