Welcome to the Age of the Engineer-Data Scientist

The growing enthusiasm for a new hybrid role raises significant questions. We answer them here.

The typical product development/simulation engineering team now enjoys access to a wealth of data that can and should be informing their product design and manufacturing processes. However, finding critical insight within these vast reservoirs of information is another matter. New skill sets are urgently needed. Specifically, engineers must be able to harness artificial intelligence (AI) and machine learning (ML) to support and accelerate better decision-making.

This fundamental shift is illustrated by the emergence of a new hybrid role — the engineer-data scientist. What’s more, the success of these multi-skilled pioneers will be crucial to the future of the enterprises that are recruiting and training them. Ultimately, engineer-data scientists must shoulder the task of turning the undisputed potential of AI and ML into faster time to market, and they must design more efficient products that perform better for customers and end users.

It’s a big ask, and the growing enthusiasm for the new role raises significant questions. Are engineers really the best people to pick up the data science baton? If they are, what skills do they need? On a more practical level, how can they acquire the mindset and capabilities of data scientists? What are the implications for organizations?

In engineering and beyond, the data science revolution is gaining traction. A recent PwC survey reports that 86 percent of respondents describe AI as a mainstream technology within their organization. However, in many respects, we have only scratched the surface. A Capgemini report reveals that by deploying AI at scale, automotive OEMs could increase profitability by 16 percent. There is frustration, too. The aforementioned PwC survey also notes that 76 percent of organizations are barely breaking even on AI.

In the search for better return on investment, the creation of the engineer-data scientist is a significant landmark. It reflects growing recognition that solutions should be driven by domain expertise. In other words, the people with granular understanding of the metadata and engineering challenges are the best people to apply the tools that will uncover insight and can thus navigate the best route forward.

Are engineers a good fit for the role? There are convincing arguments in their favor. To start with, although the impact of AI and ML will be revolutionary, it also represents an evolution from what has come before. There are clear parallels with the principles of established engineering techniques such as experiment design, as well as modern simulation and optimization tools.