From Apple to Boston Bruins, how one data professional combined his personal and professional interests

From Apple to Boston Bruins, how one data professional combined his personal and professional interestsJosh Pohlkamp-Hartt shares five tips he used to land his dream role at the Boston BruinsAndrei LyskovBlockedUnblockFollowFollowingJun 14The Boston Bruins are the fifth most valuable NHL franchise, valued at almost $1B by Forbes.

With six Stanley cup victories, the team is able to produce over $200m in annual revenue.

Josh Pohlkamp-Hartt has been a hockey fan for as long as he can remember.

Thus it didn’t take much to persuade him to leave his Statistician role at Apple to join the Boston Bruins as a data analyst.

Before working at Apple for over 3 years, Josh completed his undergrad, Masters, and PhD at Queen’s University in Statistics.

In this article, Josh shares five tips he learned throughout his career.

This includes combining interests with technical abilities, having a goal in mind, learning to communicate effectively, knowing your problem space, and developing a love for learning.

#1 Study whatever you want, but make sure you combine your personal and technical interestsThe question of which degree produces the best data scientist is largely irrelevant to Josh.

In his career, he’s met data scientists from all types of educational backgrounds.

The thing that distinguished the best ones was always their ability to combine their interests with technical abilities.

For Josh, this was hockey.

While doing his PhD, he had the opportunity to work with an OHL team in Kingston.

It started as an opportunity to collect data for independent research on hockey player valuation.

After a while, it turned into creating regression models for player performance and reporting stats to the coaches and management.

It was a great way for him to see the stuff he learned in the classroom translate to real life.

This early opportunity is also what would lead him to his current role at the Boston Bruins.

If you’re passionate about something you’ll already understand the data and what makes sense in that context.

This gives you an edge when building models and performing data analysis.

#2: Have a goal in mindWhile in grad school, Josh knew that he had no interest in becoming an academic.

Instead, an interest in building problem-solving skills and learning is what motivated him to pursue grad school studies.

He always knew that he would end up in industry.

Which is why he set out to combine what he learned from school with real-life experiences, like working with an OHL team.

It also meant that a year before graduation, instead of applying for postdoc roles like his peers, Josh began to prepare himself for interviews in industry.

Thanks to his preparation, he was able to land an offer at Bank of America.

Having this offer in hand made his interview at Apple that much easier because he knew he had a backup.

Due to this mindset, he was able to secure a job at Apple after graduation.

As he worked at Apple and gained crucial industry experience, he always kept his dream of working in Hockey in the back of his mind.

That’s why when he was contacted by the Boston Bruins for a position, Josh engaged without hesitation.

Thanks to his previous background working with an OHL team, and his professional credentials at Apple, he was seen as a high-value candidate worth pursuing.

#3: Learn to communicate effectivelyThe key to effective communication comes down to being concise and simplifying where possible.

The maxim that less is more holds true in most situations.

This also applies in the domain of models, as Josh learned during his experience at Apple.

Simple models with fewer assumptions are often much better in a business context due to their interpretability, despite their potential drop in performance.

A business stakeholder is much more likely to put trust in a model that they understand, than a black box that you struggle to explain plainly.

Another important tip is to avoid burying the lead by starting your presentation and analysis with the conclusion.

In other words, have a clean narrative that uses a pyramid structure in its delivery (i.


start with the most important and work your way down).

Yet it’s also important to keep in mind that you can’t build communication skills without some trial and error.

That’s why it’s recommended you start early by learning to write through a blog, and public speaking by joining an organization like Toastmasters or a paper/research sharing group.

#4: Know your problem spaceTo be effective in your role, you need to deeply understand the problem space you’re working in.

The best way to do this is by learning how to ask good questions and being an active listener.

This is especially important because people rarely document well.

In such cases, you must work like a detective to figure out the hidden tribal knowledge.

Joining a company or transitioning into a new role is an especially important time to practice these skills.

A good analogy is being the new kid on a sports team.

In this context, it’s always better to sit back, observe, and understand the dynamics within the team before you start holding opinions and speaking your mind.

For data scientists, a large portion of the role is helping decision makers make educated decisions.

As a result, you need to understand where they are coming from, and where they would like to go.

By having this context, you are much more likely to deliver results that are impactful.

Another area where knowing your problem space is important is in data cleaning, which is a huge component of any problem you’re working on.

Armed with domain expertise, you’ll be much more inclined to take certain steps at the data cleaning stage that will increase the utility of the dataset for future work.

#5: Develop a love for learningData science is a field that continues to grow and expand, so it’s not a surprise that developing a love for learning is critical to your success.

Josh recommends starting with the fundamentals by reading books like Elements of Statistical Learning as well as foundational papers in your field of interest.

Remember that nobody is born knowing how to read a research paper; these are skills that you acquire through deliberate practice.

Once you have a solid foundation, you can expand to the periphery and dive into the state of the art.

Arxiv is a great place for this, as it hosts plenty of pre-print papers from academics and practitioners around the wo 0rld.

Recreating these papers is also a great way to understand and accelerate your learning.

Another tip to keep in mind is to avoid using toy examples that tutorials provide and instead use real data that you’re interested in or plan on using at some point.

This will make it easier for you to get past the tough stages in the learning process and may inspire you to extend the tutorial to novel applications and insights.

Data Minds is a series that profiles professionals working with data.

In this series, you’ll learn about their story, day-to-day, and advice for others.

Previous interviews include data scientists from Red Bull, Open Door, Snapchat, Netflix and Lyft.

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