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Employee Spotlight: Meet Stephanie Kirmer, Senior Machine Learning Engineer

Zohra Veach, June 4, 2024

Welcome back to our employee spotlight series, where we showcase all of the people who contribute to making DataGrail an outstanding workplace. 

Today we are introducing you to Stephanie Kirmer, Senior Machine Learning Engineer here at DataGrail. She has almost a decade of experience building machine-learning solutions. Before going into data science, she was an adjunct professor of sociology and higher education administrator. 

Stephanie will be using her unique mix of social science perspective and deep technical and business experience to speak about the challenges facing the machine learning and AI industry today at the upcoming AI Quality Convention on June 25th in San Francisco. 

What will you be speaking about? 

The conference is centered around how to add more safety and quality control to the world of AI. There are lots of different angles and concerns in the space including ethical considerations and misinformation. 

I’ll be speaking about how AI and machine learning can be done with data privacy in mind– how you can use large amounts of data without putting customer data at risk or violating people’s privacy. There are many laws governing data privacy and security, but many practitioners in our field aren’t fully aware of them or don’t realize that they’re important. It’s actually not that scary and if you do things the right way from the start, you’ll be able to keep innovating while ensuring that your data and business aren’t at risk. 

What are you most looking forward to about the event? 

I really like speaking in front of audiences. Giving talks has been a big part of my career: I was a teacher before I got into data science. I especially enjoy opportunities to speak to people in person to get the energy from the audience when I talk about something I’m really excited about and interested in. It’ll be great to meet people who are excited about this space and care about the complexities around responsible AI. 

How did you get into this space in the first place? 

I studied quantitative sociology in grad school at Portland State University because I was interested in the intersection of data and real people– how we can use data to figure out how people interact with the world. In school, I used big survey data to better understand what is going on in real people’s lives and solve problems, and I later worked at DePaul University and the University of Chicago looking at how data can help us better support student success. Machine learning was a natural transition from that, because in ML we are still trying to use data to understand (and predict) phenomena in the world around us. In machine learning, there is always a human component: you need to know it’s helping people, otherwise, what’s the point? 

What advice would you give to women looking to break into tech? 

You have to be constantly curious and always be looking for opportunities– ask for them and seek them out. Don’t buy into conventional wisdom about what you should do next, but instead follow your own ambitions. Get to know your colleagues so that you develop a professional network that can help you find those opportunities. Also, job hunting in tech is hard. If you are just entering the job market, or even already in your career, practice interviewing as much as you can. The first few interviews will almost certainly be tough but you’ll learn from them. Don’t give up. 

As a subject matter expert yourself, what have you learned or what has surprised you about working at DataGrail? 

Tons! I have been a user and consumer of data at many companies, but before DataGrail I never felt like I had the chance to learn about regulation surrounding data the way I wanted to. I’ve learned so much about the rules and laws regarding data privacy in my time here– this is a big blind spot for people in the machine learning space and these laws have a direct effect on what we can do with data. DataGrail has given me access to experts in this space which allow me to think about ways we can better protect data and give organizations the tools they need to do the right thing. 

What have been your biggest accomplishments on our team?

Launching system detection modeling was a big deal to make it faster and easier for our customers to get their data correctly identified and secured. Improving some of our existing models was a big achievement as well. I also launched a monthly lunch and learn that I run to talk about what my team is doing and the why behind it which has been great for cross functional collaboration and making sure we are all speaking the same language. People across business functions are interested in machine learning, but sometimes find it intimidating, and I like making it more accessible.

What is the day-to-day of your role like?

We are a small team, so my day-to-day varies depending on our current project focus. We have a daily standup to start the day and then I usually do deep focus work: data cleaning, data investigation, and working on building and deploying models. I evaluate how models worked in the past and look into ways to improve them, and I work with the Engineering teams to deploy new models and better understand the product architecture. On Tuesdays, I go into our Chicago office to get in some face time. 

Learn more about Stephanie on her blog here.  

Want to work with Stephanie? Check out our open roles here.

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