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 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.
In the past year, I have had analytics & data science candidates declined for positions for a range of reasons. These included not being experienced enough, being too experienced, not having several years of experience with software or platforms that have only just been released, lack of academic background, no startup experience and the list goes on. Hence the reference to “unicorns.”
Without stating the obvious, the vast majority of the time a search for a unicorn ends up in the same way. Failure and disappointment. Nobody likes that combination so I contacted someone who was one of the authors of the article mentioned above and asked him his opinion on the current state of play and I am delighted to share his responses with you now.
Tom Davenport is the President’s Distinguished Professor of Information Technology and Management at Babson College, co-founder of the International Institute for Analytics, Fellow at the MIT Initiative on the Digital Economy, and Senior Advisor to Deloitte Analytics.His most recent book (with Julia Kirby) is Only Humans Need Apply: Winners and Losers in the Age of Smart Mach…. I would like to take this opportunity to personally thank him for taking the time out of his schedule to help on this article.
To read more, click here.
- Career: Training | Books | Cheat Sheet | Apprenticeship | Certification | Salary Surveys | Jobs
- Knowledge: Research | Competitions | Webinars | Our Book | Members Only | Search DSC
- Buzz: Business News | Announcements | Events | RSS Feeds
- Misc: Top Links | Code Snippets | External Resources | Best Blogs | Subscribe | For Bloggers
- What statisticians think about data scientists
- Data Science Compared to 16 Analytic Disciplines
- 10 types of data scientists
- 91 job interview questions for data scientists
- 50 Questions to Test True Data Science Knowledge
- 24 Uses of Statistical Modeling
- 21 data science systems used by Amazon to operate its business
- Top 20 Big Data Experts to Follow (Includes Scoring Algorithm)
- 5 Data Science Leaders Share their Predictions for 2016 and Beyond
- 50 Articles about Hadoop and Related Topics
- 10 Modern Statistical Concepts Discovered by Data Scientists
- Top data science keywords on DSC
- 4 easy steps to becoming a data scientist
- 22 tips for better data science
- How to detect spurious correlations, and how to find the real ones
- 17 short tutorials all data scientists should read (and practice)
- High versus low-level data science