01 July 


   5 Minutes



Hello everyone, welcome again to this series of interviews by Shapelets! 

As you know, we will be interviewing experts in the Data Science field to explore different topics in the data science community and put the Data Scientist professional into the spotlight.   

Today, we are very happy to be joined by Matthew Kirk, Principal Machine Learning Scientist at Zeitworks. He has an extensive professional experience in delivering value-added AI/ML solutions.  

In this interview, we discuss the main challenges data scientists face when providing solutions and insights to third parties. We also highlight that including the community in our data science efforts is essential, and also the importance of their contributions. Don’t miss it and have a look at the video below! 


Could you please share your professional background with us?

Matthew Kirk So, I started my career as a Quantitative Analyst in Finance. Worked at Stanley eventually, and I did really like finance that much and found myself transitioning into startups. So, I worked at startups for the past 11-ish years. My background is really mathematics and statistics, but also engineering. I kind of mixed the two of them and become the data scientist, machine learning type of person. That is what I’ve been doing for the last 11, 12 years. 


Matthew Kirk

Why did you choose Data Science?  

Matthew Kirk You know, Data science is interesting cause I was writing software, for the most part. You know, be on websites, and someone asked me “I know you have a background in mathematics, so could you do this other project?”. This was around 2009, 2010-ish, and then I quickly found myself going down the road of machines, clustering, … I kind of got pulled into it through the problems we were facing. I’ve just kind of been following that ever since. It wasn’t like a thing when I say to myself “I’m going to become Data Scientist”. It just kind of found myself on the wave pretty early.    

What do you like the most about your job? 

Matthew Kirk  The thing I like the most is really the learning aspect. You know, I went to school, I did my Masters’s degree and that’s great. But I find that in Data Science, Engineering and Software I’m just constantly having to reinvent myself. The technology that I use today, is so different from the technology I used 10 years ago. And I really enjoy that, I enjoy learning what’s happening in the community. I also really enjoy learning from other people. So I find that the collaboration and the social aspect is a lot of fun to me and that’s what I really like.  

Who is your role model?

M.K. You know, this is a hard question. There are a lot of people I look up to in the community. But I think the role model for me, is my dad. My dad is a huge role model for me because he was not college-educated, and he was super smart. And I find that I embody a lot of what he did. I wake up early in the morning, go get that attitude, I think it’s really important to, especially data scientists, we just need to keep going, and keep trying, just that positive attitude. 

What advice would you give to data scientists?

M.K. I get asked a variation of this question in workshops and events. And I think it’s so personal. What I would recommend is to really understand your own personality, and yourself. Like, “What are your strengths and weaknesses?”, and get really clear. So, when it comes to data science, I really struggle with some of the higher math. I find that the really complicated mathematics is tough so I tend to focus more on learning that stuff. But for other people, they would struggle with programming, so they have a hard time writing Python, so have a hard time with that. It really depends on the person. So I very much recommend the approach of taking programming, mathematics, statistics, communication, and really rank yourself as to “Am I good at those?” and from there, start to work to improve yourself. So work on the weaknesses and lean on the strengths.  

We are all different, we all have different gifts to bring to the world, and strengths and weaknesses. And I think it is better to know that.   

What are the main challenges for data scientists nowadays?

M.K. The biggest challenge I see right now, something about the 80% of the projects do not get into production. The biggest challenge in Machine Learning or Data Science is shipping it. There is a bit of community right now, which I think is starting to get there. But it is a huge challenge. My recommendation is to learn cloud computing, operations, scaling… It is hard but it’s so important. Because really, we are not done as data scientists until we impact the user.    

What do you value the most in a data analysis technology or software? 

M.K. I find that when I am looking at new software to adopt, I really appreciate companies or organisations that build a good community. So, it is interesting that big companies have great tools, but open-source and building community is really important. Because you may have the best piece of technology in the world, but if nobody uses it, it is like nobody cares. I look at the community and what people are saying about new technology. Some companies are very inclusive and will listen to the people. And some others are like “You should love the work we do”. So some companies that are really hard to work with and I think is just like an attitude problem.  . 

What are the main issues when communicating insights to the business area?

M.K. Yes, this is a big challenge for data scientists, communication. And I find that there are a lot of challenges, especially with data science. One of them is that the area we are operating with has a lot of terminology. So, I find that I have to spend a lot of time walking back into terminology that people already have. Sometimes it is almost like to talk to a 5-year-old. I mean, I am not, but it really is like that, using terms that everybody understands. That is a big challenge. I think also setting up expectations is tricky with Data Science. Unfortunately, there is being a lot of articles about data science and machine learning reinventing the world. So, people expectations are really high of Machine Learning and AI will do all these amazing things, and I have to be the bearer of bad news and say “No, it is not”. So terminology and setting up expectations are the hardest problems.   

What approach do you follow on a data analysis-based project?

M.K. There are a couple of approaches to flow around a project. There is CRISP-Dm that people use. I tend to follow a simpler version which is TACT: target, arrange or acquire data, compose, and transmit, like ship it. That is the approach that I take. I find that getting the goal first is so important for data science because, otherwise, it’s just experimentation, which is good too but in a different way. I think it is better to have a frame of what are we going to do and follow from there. 

What skills does a data scientist need for 2022?

M.K. I think that the two main skills are, first operations are really important to learn it. Understanding operations, it could be local or not, just understanding it enough. The second thing I would say is communication skills. Being able to communicate effectively is huge in data science. Even if you have the best model in the world and nobody is paying attention to it, it is kind of it was all for none. I would say that other data science specific things, if someone is trying to stretch themselves into new arenas, I think that reinforcement learning would be the first thing that will probably take off. This is my prediction. So, Torch and PyTorch in reinforcement learning is a very interesting project. So I’m focus on these things right now..


Thank you Matthew for taking the time to chat with us and for sharing your experience!  

This interview really helped us to deep into the big challenges data scientists and data professionals face in their daily work. Moreover, it was very helpful to analyse the issues that rise then communicating insights to stakeholders.  

As Matthew points out, it is key to set up expectations and use terminology that everybody understands. Besides, it is important that data science companies build a good community and listen to their audience, to be inclusive about new technologies. 

Do you want to participate and share your experience in “Behind the Data Scientist”? Contact Shapelets at and tell us your story!   

Our goal is to help data scientists and data professionals build a strong personal brand and advance their careers. Join and start now!

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