Why did you get into data science?
We wanted to know what led the Edge Team to their current role(s) as data scientists, ML engineers, and/or software engineers!
Edge Analytics is a boutique data science consulting company helping partners — who range from Fortune 500 companies to early-stage startups — turn their ideas into reality by delivering elegant solutions to complex, ambiguous problems. We specialize in data science, machine learning, and algorithm development both on the edge and in the cloud. We consult throughout a product’s lifecycle, from quick exploratory prototypes to production-level AI/ML algorithms. The Edge Team is made up of data scientists, ML engineers, and software engineers.
We were curious what led the Edge Team to their current roles, so we asked them: “Why did you get into data science/ML/SW?” We were so enamored with the responses that we had to share them. While everyone’s background is completely unique, we found a lot of similarities: namely, everyone on the team is insatiably curious, loves solving problems, and wants to build great products.
Check out the responses below!
“My foundation is in digital signal processing. While at Jawbone, we started experimenting with ML tools as part of our DSP pipeline. I first saw hints of the black-box magic of ML there. After Jawbone, I decided to go back to grad school to better that black box, and build on it.”
“I got into data science because I was interested in using real world data to solve problems that could have a positive impact on the world. I also find that developing novel algorithms that get shipped to the world is a very exciting process.”
“You can apply your skills to interesting problems across a multitude of domains.”
“Much of my PhD was centered around finding patterns in large, unstructured datasets, and building stories around those patterns. Pursuing a career in Data Science and ML was the obvious way to apply those technical skills ‘in the real world’.”
“I enjoy working with signals and building firmware or software tools for analysis.”
“In grad school, I focused a lot on DSP and applied math techniques in medical imaging. This was implemented largely in MATLAB and then Python. The work necessitated bespoke and performant code, so this got me into low-level numerical work in C and Rust. This in turn got me hooked on topics such as programming languages, type theory, and compilers. After grad school, I had to build these algorithms into products at a startup, which triggered learning and interest in larger scale software engineering, architecture, and best practices. Finally, my original applied math/numerical Python experience was very ML-adjacent, and in recent years I’ve been drawn toward the huge ML application space.”
“As an engineer, I always liked to solve problems and for years I worked on the hardware side of wearable devices. When I was training for ultramarathons, I had a lot of wearables data that I wanted to analyze. This inspired me to shift my focus in my PhD towards data science and ML in wearable sensors. The rest is history!”
“I mostly view data science/ML as a tool to work on compelling problems with interesting people. There are a lot of interesting problems that can only be solved by machine learning (e.g. in the fields of language processing or synthetic biology), so learning it is a great way to learn about and work within those fields.”
Edge Analytics is a consulting company that specializes in data science, machine learning, and algorithm development both on the edge and in the cloud. We partner with our clients, who range from Fortune 500 companies to innovative startups, to turn their ideas into reality. Have a hard problem in mind? Get in touch at info@edgeanalytics.io.
