As someone who worked in the private sector for 11 years and has now worked as an academic for 11 years, I can look back on my time as a practitioner and recite with conviction and authority the well-worn adage, “If I had known then what I know now, I would have done things differently.”
Several points of reflection here are simply related to maturity and experience. But the most tangible thing that I would have done differently is I would have engaged in meaningful collaboration with one of the many local universities in the cities where I worked. It seems obvious now, but I did not appreciate the opportunities for collaboration almost right on my doorstep.
The private sector/university collaborative partnership is mutually beneficial and, in the current environment of resource-scare data science talent, is more important than ever – to both parties.
This is true for several reasons:
At present, there is a misalignment of demand for analytical talent and the supply of graduates with deep analytical skills. If you work for a company that is struggling to fill analyst/data science positions (and that would be everyone), consider supporting your local university’s analytical “farm system” by providing them with some real data, real case studies and real problems to be used in the classroom. This helps the professors craft lectures and exercises that are aligned (potentially customized) to develop the talent that your company will need. Ultimately, the academic community cannot bring real world experiences into the classroom without real world data.
In addition, consider coming in and guest lecturing – professors love this. Guest speakers who present case studies/examples, particularly with actual data, help reiterate concepts and ideas that the professor has likely been working on with the class all semester. And, it’s like being a grandparent – you can just come in, have an enjoyable encounter with the students and then leave the difficult work (the grading) to the professor.
Any university with an analytics program likely has some kind of analytics consulting center. Working in these centers is often a requirement for graduate students. Consider presenting some of your analytical challenges to one of these university-consulting centers. Let them set a group of graduate students loose on your problem. You will likely find that because they represent a fresh and untainted perspective that their solutions might translate into an approach that would have never been considered by long-time practitioners in your company. And, depending on your internal communication policies, there could be an opportunity for a white paper or even a peer-reviewed publication – which of course the professor will appreciate.
Students love “hackathons,” data shootouts and analytical competitions. These types of events are great ways for the students to test their nascent data science skills and, importantly, get bragging rights over their classmates for a winning model. But these events are also great ways for companies to “screen” candidates – to not only see what the students can do with their data, but also create a real environment of time sensitivity and intensity of competition.
Back to the talent gap – demand is outstripping the supply. Finding the talent is difficult enough – retaining the talent is even harder. If you are fortunate enough to find new talent and bring them into your organization, they are even more valuable to you and to the rest of the market after you train and develop them. Increased salary offers are a given. So, talent retention strategies have to include a great deal more than just salary. Locality is another important factor – you are more likely to retain a student who grew up in the area and graduated from a local university than an exotic “import” from another region of the country.
So, consider your professional network. Consider your email inbox right now. How many of your contacts have a .edu at the end of their email address? Reach out to your local university and start the conversation.