DataCareer Insights: Interview with Carmen from PwC

What’s your role at PwC and what kind of projects are you currently working on?

I am a data scientist at PwC's Digital Services, which means that I am responsible for gathering, cleaning, structuring, modelling, analyzing and ultimately visualizing and presenting data for clients in various areas, such as life science, financial services and the public sector. A vital part of my job is also to drive digital innovation within PwC and to equip our people with the right tools to handle their own but also their client’s data in the digital area. Therefore, a big project that I have been working on for the last couple of months is to deliver Data & Analytics training sessions for our employees. So far, I have trained around 400 people in 4 months. Next to this, I mostly engage in so-called “smart automation” projects, where we try to increase efficiency for certain – usually very repetitive- processes by automating (parts of) them.


You have a background in biochemistry & biostatistics. What motivated you to start a career in data science/analytics?

When studying life sciences (for me it was biochemistry) one usually ends up generating loads and loads of data that then need to be analyzed in order to generate meaningful insights. To me, the latter was always the most exciting part, the part where you get to be a very creative sort of detective. This thrill of the unknown (what might the data reveal?) always fascinated me. Making this passion my job meant that I now get this thrill on a daily basis, that I get to look into so many different areas, that I get to work with so many different people with all kinds of backgrounds. And, looking at the fast-paced digital environment nowadays it will for sure be a job that will remain interesting for the next couple of years.


« Be curious, stay critical and become disruptive »

Carmen Schwarz, PwC, Data & Analytics

Carmen is a data scientist at PwC's Digital Services. She has worked with clients from various areas, such as life science, financial services and the public sector. A vital part of her job is to drive digital innovation within PwC.

DataCareer Insights

DataCareer Insights is an interview series that features experts working in the field of Data Science, Data Analytics, Business Intelligence, Data Engineering and related fields. It provides valuable insights to students and professionals interested in entering the field or thinking about the next career step.

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What excites you most when working with data?

The creativity, the fact that you are not only allowed, but required to remain curious, critical, to think outside of the box and be unique. Moreover, no project will be exactly the same even if it is in the same area. You will for sure always learn something new and most of the times the learning curve will be very steep.


Which skills would you regard as vital in your current role and which technologies are you primarily using?

Flexibility, curiosity, proactivity, critical thinking and having an innovative, disruptive mindset are the most important skills in my role. I am primarily using Alteryx, R, Tableau, PowerBI and SQL.


People debate how the optimal composition of a data science/analytics team should look like. What backgrounds do your teammates have and what do you think the optimal composition of a data science team should look like?

In my team, most of us have a pure science and/or quantitative science background such as mathematics, physics, statistics, engineering, IT etc.

I think it is important that we all have a technical and quantitative background, which gives us a common language and a common touch base. In general I believe, that innovative ideas result from a diverse and unique mix of profiles and an open-minded team culture. The more you have this kind of coexistence, the better the ideas will get. However, you always need people with a clearer focus on the business side, in order to bring those ideas onto the market.


Which three pieces of advice would you give to aspiring data scientists?

  1. Be curious, stay critical and become disruptive.
  2. In the first couple of years, learn as much as you can in as many different industries with as many different technologies as possible.
  3. Network as much as you can, the data science community is like a big family!


Thank you for your time!



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