Data Science jobs to inspire students

By Gemma Coleman. Posted

Three individuals explain how they use data science in their jobs and share tips for students interested in following a similar path

As Simon Johnson explains in Contextualising data science, providing students with context about why they are learning something is a valuable engagement tool. Here, we profile three individuals who use data science skills and knowledge in their jobs. You can use these profiles to help inspire your students when introducing data topics, helping them to understand the variety of career paths they could take with a grounding in data science.

The Games Product Analyst

Name:

Daniela Fontes

Job role:

Product analyst at Star Stable Entertainment, Stockholm, Sweden

What did you study and where:

MSc in computer engineering with a major in AI and multimedia from UTL, Instituto Superior Técnico, Lisbon, Portugal

Describe your job and how you use data science:

In my role, I work closely with game and product teams, in order to help them understand and monitor current performance, as well as optimising based on quantitative information. It starts with having a good understanding of the game and the business. Other common tasks involve:

  • Making sure that the team is collecting the necessary data (analytics specification/data taxonomy)

  • Querying existing data sources, performing analyses, forecasting (here it’s of the utmost importance that you have a good data science toolbox and know which tool to use where!)

  • Designing experiments

  • Presenting findings, reports, and results of experiments

  • Improving my team’s data literacy

What do you enjoy about your role:

Being a product analyst allows me to combine my passion for business and strategy with analytical skills and communication. I get to work on continuously improving games and products, with the goal of creating the best user experience. Last but not least, I get to work with amazing people from many different backgrounds.

Do you have any advice for students who are interested in a similar career path:

Game teams often work like start-ups. At the end of the day, your mission is to add value. As an analyst, you should always be thinking about the return on investment (ROI) of your initiatives. However, you should also block time for being creative by exploring and learning. Every analyst I have met has a different story and path, but at the end of the day, all analysts have a combination of hard analytical skills (programming, statistics, and so on) and soft skills (communication, writing, etc.).


The Lecturer and Researcher

Name:

Ciira Maina

Job role:

Senior lecturer at Dedan Kimathi University of Technology, Nyeri, Kenya

What did you study and where:

Electrical engineering at the University of Nairobi, Kenya (BSc) and Drexel University, USA (PhD)

Describe your job and how you use data science:

As part of my job at the university, I lead the Center for Data Science and Artificial Intelligence, where we apply data science to various problems we are trying to solve. Some examples include working on an environmental monitoring project, where we designed a system to collect acoustic data from nature to try and determine ecosystems that are threatened. We do this by using machine learning to classify bird sounds in the recordings and then use this information to see if there are changes in birds species’ presence. This is a direct measure of ecosystem degradation. Another project involves monitoring rivers in Kenya to try to understand the state of the catchment areas. For example, if the catchment has been degraded and trees have been cut, there is increased surface run-off, which we are able to detect with our sensors. 

What do you enjoy about your role:

We are able to apply our skills to areas such as environmental conservation and health, which could directly increase quality of life.

Do you have any advice for students who are interested in a similar career path:

Discover your passion, and find problems that you can work towards solving.


The Machine Learning PhD student

Name:

Minttu Alakuijala

Job role:

PhD student in machine learning at Google, Inria, and École normale Supérieure, France

What did you study and where:

Computer science, University College London

Describe your job and how you use data science:

I do research in machine learning to make robots a little smarter and more helpful. There are two main ways to do this: we can teach robots with demonstrations (supervised learning), or we can let them learn on their own through trial and error (reinforcement learning). My research work combines both ways, giving just enough demonstrations to get the robot started, and letting it improve itself autonomously. For now, our robot arm can solve simple tasks, like pushing cubes around, picking them up, and placing them in a bowl. However, the advantage of using machine learning instead of classical robotics is that learning methods are very general and could be applied to any task that we can collect data about!

What do you enjoy about your role:

As a PhD student I have a lot of freedom to work on interesting research questions. My research is exciting because it has so many applications in the real world. Who wouldn’t like a robot assistant to help with chores at home? Even though Roomba vacuuming robots already exist, there is so much more to do in this fast-growing field! In particular, using data science and machine learning to control robots is really useful in that it allows people without a background in control engineering to teach robots.

Do you have any advice for students who are interested in a similar career path:

If you think you might enjoy delving deep into a topic and studying it in a lot of detail, research is a great career choice. It allows you to be creative and think outside the box, constantly learn new things, and follow your curiosity. There are many research positions in data science and machine learning in companies, too, so you have options in both academia and industry. If you are interested in pursuing this path, I would recommend getting research experience as early as possible, as this will be an advantage when applying to master’s, and especially PhD, programmes. Some ways to get involved during your undergraduate studies, or even earlier, could be taking a summer job as a research assistant or contacting a professor to propose a project of your own.


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