Tell me a little about your first role within LMI.
I was originally hired to do two things: develop inventory management algorithms for a client (our Peak & Next Gen product) and start LMI’s first academic partnership program within the LMI Research Institute. While wearing both of those hats for over four years, I became interested in how the commercial space was developing software. I took a short break from LMI and moved to Boston to work for a company that produces software used heavily in engineering classrooms for math and computational purposes. Yes, I moved BACK to the cold weather! But it was a great opportunity to immerse myself in the ins and outs of engineering software and how it’s used for machine learning.
After spending a year up in Boston, you returned to LMI. What was different about your role this time?
My second time returning to LMI was within service delivery, where I learned about how to run a company and its operations. I advised project leaders and their teams on how to make business decisions and optimize metrics
What about this job did you like or dislike compared with your previous roles?
I became familiar with contracts and accounting processes and how to maximize program performance, but it was missing the heavy math that I enjoyed doing in the past. I guess you could say that the ‘advanced analytics’ component wasn’t there. That’s when I decided to leave LMI for a second time for a role that focused on machine learning, which I did for a little over a year.
What did working within machine learning teach you?
I learned how organizations could apply machine learning both internally and for customers. I also started an academic partnerships program, which enabled me to become involved in research, internships, and campus laboratories.
You came back to LMI for a third time after the machine learning role—why?
The machine learning role taught me how to detect fraud and stop financial ‘bad guys’ — all of which involved using math-based tactics and advanced analytics. About a year into this role, I learned about the academic outreach position at LMI. While interviewing, a former LMI colleague reached out about the data engineering position as well. It was a perfect duo.
What’s your favorite part about your ‘double director’ role?
The LMI roles became the perfect blend of partnering with academia, flexing my math muscles, and being a champion for a team of people. It was my dream job, as it brought together all of my favorite things to do. Plus, I really missed all of the people at LMI!
My current positions give me the chance to recall my experience in building relationships with universities while fine-tuning my advanced analytics hat. I also have a certificate in leadership coaching, so the opportunity to work with a team of 40 data engineers on their professional development was too good to pass up.
What sort of projects are you currently focused on now that you’re back?
For academic outreach, I focus on developing a strategy around prospective partners and how we will work with them. Our goals are to build a pipeline of talent to LMI and access research that will help our customers.
For data engineering, I focus on two offerings. The first is ensuring the explainability, fairness, privacy, and accuracy of data and models. The second is analytics training for practitioners and decision-makers. This will help data scientists keep up with the latest trends and federal executives develop actionable plans to immediately apply and benefit from advance analytics in their organizations.
Tell me something interesting about you that most people may not know.
I actually own my own company—I teach yoga and boot camp classes and am very into fitness. I have always wanted to be a CEO—I suppose I’m already there!
What piece of advice do you have for those people looking to start a career within advanced analytics?
First, learn how to code as early as possible. The math that I used in undergrad was all theory-based — and done with a paper and pencil — and I quickly realized that I had to find a way to translate it into a programming language for it to be useful. Learning how to code early on will prevent you from having to backtrack.
I’d also say to really get to know your customer. To develop a good solution, you really have to understand the problem first.