It's quite possible that the only thing more confusing than defining data science is actually hiring data scientists. Hiring Data Scientists and Machine Learning Engineers is a concise, practical guide to cut through the confusion. Whether you're the founder of a brand new startup, the senior vice president in charge of "digital transformation" at a global industrial company, the leader of a new analytics effort at a non-profit, or a junior manager of a machine learning team at a tech giant, this book will help walk you through the important questions you need to answer to determine what role and which skills you should hire for, how to source applicants, how to assess those applicants' skills, and how to set your new hires up for success. Special emphasis is placed on in-office vs remote hiring situations. Additionally, there are interviews throughout the book with experienced DS and MLE hiring managers lending their perspectives on the difficulties in hiring and effective strategies to hire the best teams.

What are the new and emerging applications of AI, ML, Data science?

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).

This article was written by Richard Downes. Richard is a Specialist Recruiter / Headhunter in the areas of Analytics, Data Science and Artificial Intelligence / Machine Learning and NLP (Natural Language Processing). His work within Analytics covers Predictive Analytics, Consumer Insight / Shopper Insight, and Loyalty right the way through to Credit and Risk. Four years ago an article was written for the Harvard Business Review by both Tom Davenport and DJ Patil entitled "Data Scientist: The Sexiest Job of the 21st century."The With this evolution, I now see the waters have become even harder to navigate, with companies now looking to hire people proficient in an even greater number of disciplines.

Data and analytics leaders face a dilemma. Without data scientists, venturing into machine learning and data science is difficult. Without any successful pilots, convincing the business to hire data scientists is equally challenging. Enterprises don't have to have a large data science lab in order to take advantage of machine learning. "Many organizations are still in the early phases of their data science journey and struggle to understand what machine learning and data science can do for them," says Cindi Howson, research vice president at Gartner.

Four years ago an article was written for the Harvard Business Review by both Tom Davenport and DJ Patil entitled "Data Scientist: The Sexiest Job of the 21st century." The article predicted that the demand for skilled people in this area was only set to rise and as a recruitment/staffing specialist within this space, I can only confirm that this has come to pass. With this evolution, I now see the waters have become even harder to navigate, with companies now looking to hire people proficient in an even greater number of disciplines. That is why I wanted to do this post. As with any shortage, you need to get creative but from my perspective, I am often a little surprised at how little flexibility there is when looking to employ people for specialist niche roles.