Kurt Schellhase’s fascination for the AI life cycle and a love for the fast-paced innovation of the tech industry have shaped his career trajectory.

Can you share the most interesting part of your career journey?

I’ve had a diverse and fairly unconventional career leading up to my current role at Deloitte. After obtaining my undergraduate degree in chemistry, I worked as a professional firefighter and EMT for several years. In addition to that role, I also started my career in R&D at a boutique aerospace and defense consulting firm as a material scientist. I quickly found a passion for working with cutting-edge technology, which led me to publishing several research papers, including two in the Journal of Spacecraft and Rockets, on topics including thermal protection systems, sensors, and nanocomposite technology.

While I enjoyed my work as a researcher, I decided to pursue my MBA and learn how to lead teams and how to apply my quantitative skill set to business problems.

After graduation, I started my career in AI consulting, and I found that the investigative and analytical skills I learned as a researcher proved extremely useful and transferrable to my new career as both a consultant and data scientist.

I’ve been amazed at the diversity of projects my career journey has led me to. I was fortunate to join the industry at a moment when AI was just starting to get on the radar of Fortune 500 companies, and I’ve now had the opportunity to work with clients across eight different industries. I’ve even had the chance to contribute to the development of first-ever AI production models for several companies.

What excites you most about working with data and AI?

I’ve found myself constantly interested in understanding every aspect of the AI life cycle. The process of going from the initial idea of an AI product to design, deployment, and machine learning (ML) deployment (ML Ops) fascinates me and is what keeps me interested in learning more each day. Data science and AI are broad fields where people often specialize in different roles such as data engineers, data scientists, technical project management, etc. By routinely asking myself, “How does this part of the life cycle work?” I’ve become a true AI generalist with experience in every aspect of the ML life cycle.

I find each part of the process and how it holistically fits together so exciting. From brainstorming problems AI can solve with product managers, to cleaning up “dirty” data, experimenting with AI models, and deploying ML models into production…. I always find myself wanting to dive deeper and upskill on a different aspect.

Thankfully, with the rate that AI technologies have been evolving, there’s never been a shortage of new things to learn.

Describe an interesting project that you have worked on.

I’ve been focusing more on the tech domain recently, working on a variety of use cases ranging from geospatial analytics, to scaling ML inference pipelines, and the metaverse.

In terms of the ML life cycle, I appreciate the fast-paced culture that big tech companies embrace, and how that leads to the creation of incredible, AI-powered products. In tech, there is significant pressure to innovate at a faster rate than competitors (both big companies and startups). This leads to a culture where teams aren’t afraid to move fast and fail fast. In a lot of other industries, I’ve seen the fear of an AI experiment failure leading to entire programs moving very slowly.

When developing new AI models, you must be comfortable with risk and know how to mitigate it.

What I love about the big tech projects I’ve worked on is that both successes and failures happen at light speed, leading to smaller teams ultimately delivering higher impact work than you would find in larger teams in other industries.