Designing League Tables for Major Sports in the US

Designing League Tables for Major Sports in the US

Jai is currently taking up a Bachelor’s of Computer Science at Carleton University and was successfully hired as a Product Management Intern at Flixed through Techintern’s platform. Flixed is a start-up building a universal streaming discovery layer. They do this by developing tools and resources that make streaming decisions easy and accessible.

What did you work on over the course of your internship (what projects and features did you contribute to, and did you have full ownership over any)?

Jai had full ownership over a project where he designed and developed league tables for 5 major US sports. He worked on it in order to improve the presentation of teams on sports pages by introducing relevant statistical data about each team to them.

What was unique about the company and its role?

The company’s culture prioritizes deep work over excessive meetings. Flixed believes that if one could get 3-4 hours of concentrated work done in a day without distractions and interruptions, they’d be more productive than 99% of people. As such, a lot of the work Jai did was collaborative but asynchronous, which was very helpful considering he lives in Kenya and was working remotely.

The role itself was very interdisciplinary - his official title was a “product management intern,” but he was allowed and encouraged to dabble with design and software engineering as well. This helped him develop his skills in a variety of ways and gave him a greater understanding of how software companies operate.

What did the company do to mentor and support you during the internship?

From the first day, Jai felt included in the company and was allowed to share opinions and ideas on various company affairs, even if they were outside his scope of work. He always felt welcome sharing new ideas with his coworkers, and they were very helpful when fleshing them out, or when he hit technical roadblocks.

Flixed also designed a 4-week “curriculum” that helped him get up to speed with the company’s codebase, projects, and values so he could get a better understanding of how the company works and where he can potentially contribute.

How could Techintern.io have better supported you during your internship?

Jai thinks that Techintern.io was the perfect platform - the chat feature helped him schedule interviews conveniently and it has a range of opportunities that often go unnoticed on more conventional job boards like Indeed or LinkedIn. He’s glad to have found Techintern.io and got an amazing internship experience out of it.

Do you have any tips/advice for tech resumes?

Jai emphasized trying to prioritize impact points over merely listing your experiences. The full nature of your work in an internship or a project will come up in your interviews, but being able to have reasoning for why you did certain things will make your resume more impressive.

Do you have any tips/advice for tech interviews? What was your interview process like?

Jai went through a product management interview process. This involved an initial call with a take-home assignment, an interview with follow-up questions about his resume and take-home, and a call with the CEO of the company to determine culture fit.

However, to prepare for technical interviews, He’d highly suggest becoming very familiar with data structures and algorithms and using the Tech Interview Handbook.

What did you hope to gain from your internship? What did you gain?

Jai hoped to gain experience in product management, which was something he had never done before. He also wanted to learn more about the corporate world, as this was his first real internship.

Both of these expectations were met and exceeded. He gained experience in many areas of software development and grew as an engineer, designer, and product manager.

Do you have tips for future interns? (Making the most of the internship, networking, learning curves, etc.)

For learning, try to map out different “flows” within an application’s codebase. When data is fetched from a certain API call, where does it go to be rendered on the frontend? How is the data manipulated along the way?

New, large codebases can be intimidating, but looking at examples of smaller projects using your company’s tech stack can help you connect the dots on what exactly goes on within the product. The software can be complex, but there are always resources that you can use to contextualize a lot of the code you read and write.

The best thing to learn, though, is to ask several questions. Familiarity with a codebase and your team is an important part of success at any job, whether at the intern or full-time level.

Did anything else interesting happen over the course of the internship?

Since Jai’s work involved sports data, he had to do lots of research to find reliable sources of data. He found an API that seemed to have a lot of information, but some integral functions for his case didn’t work. He contacted the person responsible for developing the project and he was invited to their Discord server, where he met many people in the sports analytics community working directly at sports teams or data analytics companies focused on sports! It exposed him to a new side of the job market and strengthened his interest in statistics, data science, and data engineering.

Conclusion

  • Jai worked as product management intern at Flixed with a focus on sports programs. Flixed is a platform that makes streaming easily accessible.
  • Jai was given time in a 4-week curriculum to get him up to speed with the company's codebase.
  • Concisely explaining what you did previously in your resume will help it to stand out.
  • For interviews, he recommends getting familiar with the data structures and algorithms, and using the Tech Interview Handbook.
  • You can contextualize the tech that the company is using by looking at smaller projects in the documentation. However, it's still always best to just ask questions if there is something you're not familiar with.
  • Jai had to research on a lot of resources and evaluate them if they are reliable. This landed him on a community of other data scientists and engineers that further sparked his interest in data science and sports analytics.

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