Meet Laura Winger, a data scientist with a background in Mechanical Engineering. She started her career in large corporations, where she gained valuable knowledge to further her growth. Understand how she solves difficult challenges in her day to day life and the career advice she learned along the way.
Tell us a bit about yourself – where are you from? Where did you get your engineering degree?
Originally from Calgary, I grew up on a farm with my family. I wanted a change of scenery for my undergrad, so I moved to Ontario and completed my degree at Queen’s University in Mechanical Engineering. After two years in the engineering field, I decided I wanted to learn more about numerical computing — which led me to complete my masters at Columbia University in Operations Research. While studying in New York City, I really found my passion for data science. I accepted my first role as a data scientist at one of the largest utility companies in the US, based in San Francisco.
What made you switch to the tech world?
I never thought I would be working at a tech company, but while living and working in Silicon Valley, I realized I had a love for data, which led me to artificial intelligence. I decided my next move would be Montreal, as it’s one of the biggest AI-hubs in the world. After 5 years in large corporations, I was looking for a change of pace. I wanted to work for a company who’s rules were still, so-called “unwritten”, and to be able to contribute to writing them.
You’ve accumulated a wealth of experience at different companies by now. What would you say is the one thing that sets Stradigi AI apart?
Stradigi AI has over 30 researchers in the deep learning space which is remarkable — and something you don’t see in a lot of other companies. More specifically, what I love about data science, is that it can be applied to any industry and here at Stradigi AI, the role does exactly that: you touch upon many different verticals to solve complex problems. All while collaborating with a diverse group of people.
How do you typically think through difficult projects?
I would say there are typically three steps. Keep in mind that this is a very high-level overview — and there are many sub-steps within each part.
1 – Brainstorm: There is a blue sky of brainstorming that takes place in the initial phase, you need to figure out the problem and ensure you have the right data to solve the issue.
2 – Collaborate: Essential to work with multiple data scientists to get input from multiple people as each and every one of us brings different approaches and techniques to the table.
3 – Deliver: Give a prototype to set the expectation for your client. You must leverage your internal teams, for example, our solution and product team, to ensure what you initially prototyped can be delivered.
What is the best career advice you’ve ever received?
It is so important to stay flexible. I never planned my career path with precision. My approach was to find opportunities that would help me grow. When I first started in the workforce as a petroleum engineer, a data science career didn’t even exist yet! It’s important to know what you value and what you are passionate about so you can seek opportunities further down the line.
What advice do you have for fellow data scientists?
Start small. A lot of times people skip over the basic models, which are the most interpretable for external clients. These models, although not as fun, are easier to get buy-in because they are more willing to trust a model that they understand.