Many people today are concerned about critical infrastructures such as the electrical network, water supplies, telephones, transportation, and the Internet. These nerve and bloodlines for society depend on reliable computing, communications, and electrical supply. What would happen if a massive cyber attack or an electromagnetic pulse, or other failure mode took down the electric grid in a way that requires many months or even years for repair? What about a natural disaster such as hurricane, wildfire, or earthquake that disabled all cellphone communications for an extended period? David Brin, physicist and author, has been worrying about these issues for a long time and consults regularly with companies and federal agencies.
Are you looking to get a break in data science but struggling to clear interviews? Are you scared of getting into data science interviews? Or you just aren't sure what to expect in these interviews? You might even know the data science tools and techniques relevant to the job. And yet the employers keep rejecting you. It certainly doesn't help when job descriptions require multiple years of experience for a seemingly entry-level role! I have a rich background in learning and development, a non-technical and non-data science field.
Hiring a data scientist can be a tricky process. The actual definition of "Data Scientist" is vague, the day-to-day job of someone with "Data Scientist" in their job title varies dramatically between organizations, and people come to the field from a wide variety of backgrounds. Examining the past of a data scientist candidate is a science in itself, one worthy of a blog post of its own. Today we're going to stick to building an interview that examines the present. Most data scientist job interviews fall short of exploring the full range of topics necessary to determine a proper fit.
The Data Scientist has been called the sexiest job of the 21st Century and thus has started attracting many young, ambitious contenders for this high-paying and "glamorous" post. However, there is a growing concern among employers and business leaders that candidates are getting enticed by just the "title" without building the proper mindset and skills to succeed on the job. As no licensing or regulatory body oversees the hiring process for Data Scientists, many types of candidates--some genuine and some fake--arrive with tremendous hope and enthusiasm at job interviews. The unprepared Data Scientists are often those that show expertise in one academic discipline without comprehending that this complex science is multi-disciplinary in nature. Candidates with only advanced statistics or Machine Learning knowledge may slip through the interview cracks, but will not last long as Data Scientists unless they can demonstrate equal expertise in business practices and enterprise communication.
From predicting who would or wouldn't be a good fit, eliminating bias at the initial screening stage, scheduling interviews, communication with candidates regarding their application, even parts of the interview process – many things can be handed over to Artificial Intelligence (A.I.) nowadays and be run as well as, if not better than, a human could.