Meet Leo Orshansky, Qcue's Data Science summer Intern

Leo Orshansky has interned at Qcue on the Data Science team for summer 2018, and 2019. We had a quick Q&A with him at the end of this years internship.

Before joining Qcue for Summer 2018, what other options did you have?

Initially, I wanted to intern at Google. But I found Qcue early on in the process. I also wasn’t looking for Data Science! Software engineering has been a hobby of mine for a long time. I was looking at software internships. Data Science is a broad field. For my hobbies I had used Python, worked with AI, gaming, and designing/building websites. I also played with machine learning at school, and took a couple of advanced statistics classes so Data Science fit my interests and skills.

What were your career goals before interning with Qcue?

I wanted to be a software engineer, but Data Science is well within the realm of consideration, following the experience I have had a Qcue.

What have you been working on during your internship?

Last summer I was working on low competition pricing. That is, pricing for box offices, through Qcue’s Pricing Module to determine the best way to determine how to set prices for events based on historical demand. I was looking at historical data and seeing how well teams were doing.

This summer I have been involved in a number of much bigger projects including Distribution Market Predictions. I built models for MLB, MBA, College football and Hockey to predict revenue for an event. Also inferred sales. This is where we have inferred sales but it doesn’t work well on performers where no one lists seats on the exchange. We have tons of listing where we don’t know what seat people are in. It’s hard to tell when something actually sold because the listing disappears and another appears but we don’t know if it’s the same listing. I have been working on some algorithms to see if it actually sold.

What were the biggest differences between each summer internship at Qcue?

This year, I was actually working on the Data Science team rather than working on my own projects and reporting each week. I’m not working on product code, but I present research to the product team which might get turned into code depending on how well it solves the problem. This is how the DataScience team works at Qcue.

What’s the difference between the work you are doing as an intern, compared to the work if the Data Science team?

It feels very equal, I have an impact on the state of the data science work we are doing.

What were your initial expectations of the internship?

This summer I came in expecting to be working on my own projects and reporting to the Data Science team, however I have been far more involved in product road map work. This has been really enjoyable.

How would you describe the culture at Qcue?

Outstanding! I have talked to other high school friends doing internships here in Austin – one in particular says he doesn’t know any names of anyone in the office or the team he works with! This is a huge reflection in that I know everyone at Qcue. We have lunches together, I am included in all the company wide events, presentations, discussions. I talk to everyone. I feel like I am a team member. I do still feel like a Qcue intern but that’s a positive thing because I am always learning and people always step up to teach him stuff.

What has been you biggest success during your internship?

This is a hard question to answer because I have been doing a lot of stuff. I have been working with Dan Keshet to improve my general DataScience skills, which I am definitely better than I was at the beginning of last summer. I am really proud of my work!

 What’s next for you?

I am applying to College, to study Computer Science. My first choice is Stanford, second choice is Berkley and third is Princeton. I am really hopeful to get in. My academics are almost perfect and this internship will help a lot.

Why Computer Science?

I have been fascinated with this my entire life. I have been coding most of my life. My dream job is in a top tech company – like Google orAmazon because I know that it’s great to work for these companies as there area lot of opportunities to work on bleeding edge cool stuff such as AI. To be part of this would be amazing.

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