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From Aerospace Manufacturing to Data Science: Bob Wood Shares His Story

Two boys grew up in Saudi Arabia. Both boys lived in the same town, both moved to the United States, both attended Case Western University, and both studied engineering. And yet they never met. 

Until now.

One of those boys was Cal Al-Dhubaib, Pandata’s CEO. And the other was Bob Wood, a Data Scientist at Pandata. For this post, we sat down with Bob to learn about his unique journey from childhood to aerospace manufacturing to data scientist. 

How did travel influence your interests and choice of career?

I was born outside of Chicago. My dad was a doctor. My family moved around a lot, including several years living in the Middle East. Growing up, I wanted to follow in my father’s footsteps and be a doctor. But then I discovered how much school was involved. I did, however, have a knack for the technical side of things, so I decided to pursue a career in mechanical engineering. I attended Case Western and completed my undergraduate degree in mechanical engineering.

Where did you go after you graduated?

At the start of my career, I worked in aerospace engineering for about 10 years, with a focus on quality control. There I discovered that I didn’t want to do purely technical engineering. So I headed back to Case Western and continued my studies with a masters in engineering management.

What led you to transition from years in aerospace to data science?

The work environment in aerospace wasn’t quite right. So I sat down and took stock of my interests, and my strengths and weaknesses. I noted that, over the years, I had already been doing analysis and some informal dashboarding, using tools like Excel. I found myself gravitating towards the data science industry. I was interested in learning about new tech, new trends and advanced tools.

I drew up a list of career choices, and then narrowed down the list to the top five. One of the options was a career in data analytics. So I determined what was required to enter the field, and the path I needed to take, and then attended a Data Analytics Boot Camp at Case Western. Now, I’m a data scientist at Pandata. 

What does a typical day look like as a data scientist?

I generally have a number of projects going at the same time. I start my day with admin work, such as developing and refining documentation. Before lunch, I work on technical tasks, like identifying various machine learning models. After lunch, I do the coding to experiment. And, at the end of the day, I may mentor an associate data scientist.

What unique perspective do you bring to the team as a data scientist with a quality control and aerospace background?

I have a keen eye for detail, having read tons of detailed specifications for aerospace parts, components and assemblies. I understand industry best practices when it comes to documentation. I am organized. And I bring problem solving and logic to the Pandata team.

What are the most challenging aspects of being a data scientist? 

The most challenging part of data science for me is staying on top of new technology. New tools are being launched all the time. A lot of underlying machine learning (ML) models are the same, but things like AutoML are constantly evolving. 

What are your primary goals as a data scientist?  

A primary goal of mine is delivering solutions that either accomplish the goals or conquer the challenges that our clients set before us. Another goal is making sure the solutions we create solve for problems the right way, that they take into account ethical considerations when using someone else’s data.

What are some top trends in data science and AI that you look ahead to? 

AutoML is a top trend. AutoML takes some of the manual work out of the initial modeling process for the data scientist. This includes setting up parameters and examining metrics. 

There’s the H2O AutoML platform, for example. And Google has an AutoML platform, too.

Then there’s the emerging conversation around making machine learning transparent and explainable. How do we interpret and understand the results from an ML model, for example? How do we build trust with ML results? How do we offer ML explainability? And how do we depart from using black box models in AI? 

Do you have a favorite project that you’ve worked on recently? 

I worked on a project for the Cleveland Museum of Art, designing a public-facing dashboard that shows the artwork they have opened to the public. The museum wanted a way to offer the public access to full-resolution, digital versions of their art for embedding elsewhere (websites, for example). The museum needed to track how their artwork was being accessed and used. 

We pulled data about each piece of art that’s in the museum’s collection, and tied it into an interactive dashboard where anyone can see what’s getting used. We were given full design freedom for the entire dashboard, so it was a rewarding project. Plus, the public gets to see our handiwork.

Gain More Expert Insight

Bob Wood’s journey is just one of the many unique backgrounds that make up the Pandata team of expert data scientists. If your organization is looking for help getting started on the path to AI success or is ready to design and implement the right AI solution, schedule a complimentary AI Exploration Session with our team.

In this session, we will discuss your current needs, and dive deeper into ways your organization can implement human-centered, explainable, trusted AI and machine learning solutions to accelerate your business goals. 

Contributor: Nicole Ponstingle, COO/AI Translator at Pandata

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