The new era of the data scientist in engineering has arrived

Artificial intelligence, engineering, and data science were once disparate disciplines with little overlap, but now these specialized fields are rapidly converging. Altair believes engineers hold the key to unlocking the potential of AI in manufacturing.

Today, product development and simulation engineering teams have access to a wealth of data that should inform their product design and manufacturing processes. This means engineers must be able to leverage AI, machine learning (ML), and data analytics to support and accelerate better decision-making, reduce time-to-market, and design more effective products.

The engineering industry has been busy democratizing simulation technology within the design community for the past decade, but now we are witnessing the emergence of a new engine of democratization – that of machine learning. If history can teach us anything, it’s that democratizing technology requires a cross-functional team to succeed. What we find is that the optimal approach to scaling data science is to pair five subject matter experts/data engineering specialists with each data scientist.

Who better to come up with the use cases than the people who design these products, and who better to verify, scale, and operationalize those use cases than experienced data scientists? How many times have we heard data scientists complain about spending too much time on data profiling and reporting? Why not give subject matter experts the power and tools to solve these challenges and give your data scientists the freedom to explore niche custom model development? This way, you can leverage the benefits of a democratized solution and provide the people closest to the business with the tools to solve it while maintaining control and lineage.

The best part of the engineering data scientist movement is that companies don’t need to search for them. It is an untapped analytical resource within an organization that, with the right structure, can provide information that otherwise would not be found. We’ve all read the articles and seen the statistics highlighting how revolutionary and game-changing AI can be. At the same time, given their existing capabilities, most engineers will find adopting it a small step rather than a giant leap.

By nature, engineers are curious and enjoy solving problems. Ultimately, engineers are driven by a practical desire to build something better. Instinctively, they will be drawn to tools that can help achieve this goal, as they always have with the principles of established engineering techniques such as design of experiments, as well as modern simulation and optimization.

To give a concrete example: Rolls Royce led a cultural transformation in its organization. To date, they have logged over 78,000 training hours on drag-and-drop self-service tools. Their suite of courses included introductions to data science, AI, ML, coding and digital literacy and ranged from “small size” 20-minute sessions to fully certified extended training programs. This means that they have now successfully trained 20,000 employees over the past two years. This paved the way for engineers to embark on data science-focused projects and see the success of those projects.

McKinsey estimates that AI will add $13 trillion to the global economy over the next decade, but companies are still struggling to scale up their AI efforts. The difference between the winners and losers of this transformation will not be determined by the implementation of the AI, but by how you did it and who you involved in the process.

Sign up for our 3-part webinar series: Data Science and Practical AI for Engineers. This series contains everything you need to know to get started with large-scale data science. It was designed by engineers for engineers and will be presented by technical experts with case studies so you can see how others have successfully implemented AI.

Sean N. Ayres