Spotlight

Space Talent Spotlight: Kate Zimmerman

The Space Capital Podcast |

July 8, 2022

Kate Zimmerman

“There are key moments in your life where you have to be bold, trusting your instincts, and not be afraid to fail.”

Spotlight

Space Talent Spotlight: Kate Zimmerman

|

July 8, 2022

Kate Zimmerman
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“There are key moments in your life where you have to be bold, trusting your instincts, and not be afraid to fail.”

Spotlight

Space Talent Spotlight: Kate Zimmerman

Kate Zimmerman

“There are key moments in your life where you have to be bold, trusting your instincts, and not be afraid to fail.”

Credit: HawkEye 360

A Space Talent Spotlight Series Interview with Kate Zimmerman, Chief Data Scientist at HawkEye 360, Computer Engineer at US Air Force Reserve and former Senior Manager at AWS ML Solutions Lab


What is your background?

Early on, I found a passion for computers and programming. Programming is very methodical but can also require some creative solutions, which is something I found I could understand and really enjoy. I would program websites for fun and during high school, I received strong encouragement from a teacher to take more formal computer science coursework. I still remember my first programming class, when I was the only girl in the room. That was the first time I experienced being in a male-dominated environment and it was tough, but thanks to the encouragement of my teacher I stuck with it and didn’t let myself be intimidated.

As I was evaluating colleges, I quickly realized that college is expensive! Knowing that I was going to be responsible for paying for my college education, I started looking for scholarship opportunities. Through my research, I learned about the military academies and I ultimately decided to apply to the Air Force Academy. After only a few months attending the Academy, I knew that the aerospace industry is where I wanted to take my career. Seeing fighter jets flying over the school, getting to test satellites and launch them into space, it was inspiring. I got my undergraduate degree in computer engineering, and during my time at the Academy I got to build a satellite payload, ride in all kinds of military aircraft (helicopters with the side doors open are my favorite), and even fly a glider (albeit poorly).  

IMAGE: Air Force Graduation

After the Air Force Academy, I went to the Air Force institute of Technology and completed my master’s degree in electrical engineering. My thesis applied machine learning to a radio frequency (RF) fingerprint to uniquely identify a device. My research ultimately concluded that data quality matters for machine learning. The clean structured training data worked well, but was really hard to apply in the real world where data is much more noisy. For anyone who has worked on ML before, I’m sure they have seen this issue many times before! This was my first time learning about machine learning and it was immediately clear that this technology was going to change the world. 

After wrapping up my Masters, I went to the National Reconnaissance Office (NRO) and had the opportunity to work on satellites and satellite ground systems. One of the ground systems I was supporting was going through an upgrade and migration to the cloud. This project brought me in contact with the team at Amazon Web Services (AWS) and I began to realize that I really enjoyed cloud architecture and cloud technologies. After my time at the NRO, I transitioned from the Air Force to take on a role at AWS as a Solutions Architect, eventually working my way to leading the Machine Learning Solutions Lab for National Security customers. 

My next major step was joining Hawkeye 360. I had heard about HawkEye while in grad school and thought that it was crazy that a company could be selling commercial RF data. I ended up working with them on a project while at AWS and saw how much the company had grown and I also realized how much I missed working with space technology. Cloud technology is a lot of fun, but cloud technology plus aerospace technology - now that is a powerful combination! Hawkeye was right at this strangely perfect intersection of all of the skills I’d been building throughout my career: satellite payloads, ground systems, RF, and AI. It felt like an incredible opportunity. so I jumped at it. I am now the Chief Data Scientist at Hawkeye 360, which means I am responsible for all things data science including building the roadmap of algorithms, working with customers for beta testing, support engineering for testing plans and rollout, building cloud to support ML ops architecture, and ultimately ensuring the right tools end up in the hands of our customers.


What have been your top career accomplishments so far?

There are a few moments in my career that I am especially proud of in hindsight. During my time at the NRO, I was able to develop cloud architectures for two major ground systems thanks to the support of my colleagues, the AWS team, and reading a lot of textbooks. The NRO was my first time really getting deep into cloud technologies, and that accomplishment really set me up for a successful career at AWS.

The second major accomplishment that I am very proud of is my progression from an mid-level Solutions Architect to a Principal level architect in only 4 years, which is a rare achievement at AWS. I have had people ask me how I got promoted so quickly, and the biggest advice I can here is don’t be afraid to take risks and trust your instincts. When I was at AWS for only a few months, I got pulled into a meeting with the Chief Technology Adviser at the Office of the Director of National Intelligence. I didn’t even have my basic AWS certifications yet! In no way did I feel qualified to lead this discussion, but the adviser had specifically requested to talk to someone about machine learning and I just so happened to be the only person in the office that day with a background in machine learning. So I went for it, I took the risk that I might make a fool of myself, and turns out the meeting went incredibly well and fast tracked me for a promotion.

I hope there are many more accomplishments in my career, and I am excited for what the future holds in my new role. I now find myself in the world of MLOps pipelines and production code deployments, building algorithms to solve mission challenges, and earning the trust of analysts in our technology. It is a whole new world of challenges that I look forward to navigating.

IMAGE: Presenting at GEOINT Generations in Conversation Panel Credit: GEOINT 2022


What were the critical steps/choices that helped you get ahead?

Mentorship is important, I had help across all major steps in my life, from high-school programming teachers, to academic and military advisors, to colleagues at the NRO, Amazon, and HawkEye 360. They always helped me see the bigger picture, challenged me to take risks and step out of my comfort zone, and gave me constructive criticism when I made a mistake. 

It’s important to note that while I never formally called these folks “mentors”, they were always willing to share their perspective and engaged with me when I had questions. Never be afraid to ask for advice. I have found that nearly everyone I have reached out to has considered it a compliment that I valued their advice in my career decisions. It can be easy to get hyper focused on the problems right in front of you, but to advance in your career you need to think about those larger, long-term, challenges too. 

Mentorship can look different for everyone, and while I prefer more informal mentorship approaches there are lots of great resources for more formal mentoring programs too. Most companies will offer a mentorship program you can sign up for (and if your company doesn’t offer this program, take a risk and start one!). For those who are or were in the military, I also recommend Veterati as a great program supporting thousands of Service Members, Veterans, and Military Spouses.


What part of your education had the most impact on your career?  

This is a tough question, because I think my career today really is the culmination of all the education I have received so it is challenging to point to any one part. In general, data science and RF were the important technical foundations that allowed me to become a Chief Data Scientist at a commercial RF data and analytics company. Finding my inspiration in aerospace at the Air Force Academy was also extremely important, because it helped me find the field I was most passionate about. I will also say that, while I thought it was silly when I was in college, all that leadership training that students get at military academies has also been extremely impactful on my career. That training has allowed me to take on larger management and leadership roles earlier in my career. So discount those leadership books! Turns out there are some real nuggets of wisdom in there.

What about your career have you enjoyed the most and least?  

I have really enjoyed seeing the impact of the algorithms and systems I have built. A big highlight has to be seeing my first payload launched into space- it was pure fear and then elation when the first data packet downlinked from space! I have also really enjoyed working on field exercises, where the algorithms I have been supporting are tested on real-world data in operational exercises. Field exercises are always stressful because nothing ever goes quite according to plan but you learn so much having to troubleshoot on the fly and it is satisfying to see success in real time. My least favorite parts of my career are probably building all the business cases to justify all the fun projects I want to work on, while it's a necessary process the science is way more fun!


Where do you see the most promising career opportunities in the future?

I believe that ML and deep learning are going to see exponential growth, we really are just scratching the surface of what these technologies can do. More specifically, I see tremendous potential in the following areas:

  • Graph based machine learning, understanding the links between activities happening in the environment. This type of ML will help us achieve greater understanding of how many data sources are related - check out some of the research on graph-based social network analysis, it is really fascinating. 
  • When it comes to ML in the aerospace industry, it is still very early days. I see ML expanding in everything from aircraft manufacturing, to improving satellite efficiency, to deep space exploration. If you are interested in ML and aerospace, there are going to be lots of career opportunities in the future.
  • Big data and data engineer careers are also going to be extremely popular, before we can really do ML we need to clean and prepare the data make these roles critical to successful ML adoption


What advice/resources would you share with the next generation?

You have to follow your passion. There is no fixed path to success but if you are doing what you love, it will show in the quality of your work. When you bring your passion to work, it’s infectious and others will be motivated to work with you. And if you find your passions start to shift or evolve over time, don’t be scared of it, trust yourself. 

I started in the space industry, then focused on machine learning, then on cloud technologies, before finding myself once again in the space industry - I am passionate about all of these technologies, and that is okay!


Is there anything else you would like to share?

I mentioned military academies as the scholarship path I took to get into the Space Industry. There are also ROTC scholarship options and I also really recommend the USGIF and Space Foundation. Both provide scholarships, internships, and conferences to connect with other young professionals, no military service required. These foundations have been really wonderful for networking and a have offered a programs for me to continue to learn.




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Space Talent Spotlight: Kate Zimmerman

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