Best stated, the reason for the convergence of operational research and data science is that they both operate on big sets of data. Data science extracts knowledge from the available data while Operational Research (O.R.) uses data to make decisions. The use of the two have become very popular with each other in decision making, but do they mean the same?
They don't. And people who know both are hard to find.
O.R. and Data Science are both very unique and complex, however can give very powerful analytical power in their given applications. That said, there are challenges to finding a Data scientist that understands both O.R. and Data Science and the convergence of the two.
Sage worked with a Fortune 500 company to fulfill a Director of Data Science role. Despite the status of this fortune 500 company, they have the same burden of time, resources, and supply shortage of qualified Data Science candidates.
From a technical aspect, we found it's intuitive that candidates understand how to operate the functionalities of Gurobi and Cplex. Essentially, Gurobi is an optimizer used for optimization for linear programming while CPLEX Optimizer solves integer programming problems. Finding candidates can be a challenge when candidates only mention optimization in their profile but don't mention name of specific tool. Apart from these tools, candidates must have both OR and python production coding experience, which at times candidates don't mention in their profile.
To address our challenges we decided to focus the search on nationally recognized Laboratories, Research Labs and Academic Institutions to create a top of the funnel analytics and reach thousands of candidates. Along the way, we to address the technical challenges by identifying a candidate that had technical leadership and expertise in Data Mining, Data Analytics and Machine Intelligence.
Here are additional insights we found to be best for hiring practices for Data Science roles: