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A Day in the Life of a Data Scientist

Updated on Jul 31, 2019 1876 views
A Day in the Life of a Data Scientist

Do you know what the top skills companies are looking for?

If you say Data Science, then you are correct.

According to a LinkedIn study, data science is one of the most important skills companies are looking to hire.

Data science is an emerging and growing field and it is becoming more popular as companies seek ways to gather and get insights from tons of data that they have gathered.

So if you are thinking of becoming a data scientist or you want to know what it takes to build a successful career as a data scientist, then you will love who our guest is today.

 

What is Data Science?

According to Wikipedia, data science is a multi-disciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data.

 

Who is A Data Scientist?

A data scientist is someone who knows how to extract meaning from and interpret data, which requires both tools and methods from statistics and machine learning, as well as being human. She spends a lot of time in the process of collecting, cleaning, and munching data, because data is never clean.

 

Now meet Bernardo F. Nunes, Head of Data Science at Growth Tribe, the leading Growth Marketing Academy in The Netherlands.

In this expert piece, we asked him to share some insight with us on a variety of topics. Below is the interview.

 

1: Can you please introduce yourself to your audience?

I practice the combination of behavioral science, data analytics, and digital solutions to predict consumer behavior.

 

2: What is the first thing you do every day?

Check Instagram on my mobile phone.

 

3: How did you get started as a data scientist?

While I was getting a Ph.D. in Economics at the University of Stirling, United Kingdom, I was also a member of the Behavioural Science Centre. There I helped a set of regulatory bodies and private companies to get insights from their data and to transform ideas into empirically-informed decisions. I got proficient working with large-scale data, making use of causal inference and predictive modeling for policy prescription and product design. Before I graduated, I also got a part-time position as a Research Assistant in the Financial Conduct Authority, FCA-UK, working for the Behavioural Economics and Data Science Unit. That was when I made a transition from Econometrics to the broader field of Data Science.

 

4: What is one key mistake most businesses are making when it comes to using data to make informed decisions?

Not educating the decision-makers IN the first place. Many companies end up hiring data scientists and data engineers before they educate their c-levels and managers on the state of data analytics, especially artificial intelligence and experimentation.

 

5: If I was to start a career as a data scientist, what advice would you give to me that can make me successful?

Master the main business applications of prediction models and clustering first. They are the low hanging fruits of AI in the industry.

You have to build and communicate the results of your side projects. Social media content building (Twitter, LinkedIn), some public speaking with visual results and storytelling.

At Growth Tribe, we help companies to find business-savvy data scientists. They can hire a trainee from our talent pool or select a current employee. It is a perfect starting point for a data science career.

 

6: What skills or trait do you think someone needs to excel as a data scientist?

This is easy. Intellectual curiosity, industriousness, and need for cognition. Business-savvy data scientists (Type A – analyzer) need to also master data storytelling and visualizations. To work more on the “builders” side (Type B), orderliness and steadiness are more relevant.

 

7: If you were not a data scientist, what would you likely be?

Investment manager. I was one before starting the Ph.D.

 

“Information is the oil of the 21st century, and analytics is the combustion engine”. This quote by Peter Sondergaard says it all.

Now you have heard it directly from a Data Scientist’s mouth. Now you know what they do, what motivates them and how a typical day in their work-life is, and you don't need a PHD to build a career in data science these days.

Thank you Bernardo for sharing your insights with us.

Now over to you, if you are thinking of getting started a career as a data scientist or a data analyst, we love to hear from you. Drop your comment below.

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