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Tamara Stemberga, Data Scientist at causaLens

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ARTICLE SUMMARY

Q&A with Tamara Stemberga, Data Scientist at causaLens. Read on to learn more about Tamara’s career journey, what advice she would give to younger Data Scientists and what attracted her to work at causaLens.

Read all about Tamara’s career journey. 

Tell us a little about the career journey that brought you to your current role?

My background is in Theoretical Physics and I have a PhD on Trace Anomalies in gravity and Higher Spin Theories. In the last year of my PhD I became interested in the areas of Data Science and Machine Learning and decided to make a transition. I first joined causaLens as a Data Science Intern and then became a Data Scientist.

What attracted you to Causalens?

At causaLens we focus on Causal AI, a way to understand the cause and effect and ensure effective human-machine interaction to solve a wide range of business problems. When I joined, it was a small and dynamic startup where a major part of our activity was research-oriented, and it offered numerous opportunities for learning and personal career development. My colleagues were extremely friendly and welcoming, creating a fantastic working atmosphere.

What made you pursue a career in Data Science?

Data Science and Machine Learning has emerged as an exciting discipline of rapid growth with an increasing number of applications and uses in everyday life. It is both an area of significant industrial applications and interesting academic research. Coming from theoretical physics, it appealed to me to be part of such an attractive mix of both worlds. I wanted to work in a challenging environment where every day we tackle new problems and come up with novel methods and unique solutions.

What advice would you give to younger, aspiring Data Scientists?

I would say that it is hugely beneficial to develop a deep understanding and intuition for the mathematical and statistical background for Data Science. It will allow you to effectively understand real-life problems and translate them into mathematical models. Try to think abstractly and laterally: it will help you to generalise methods and solutions to a broad range of applications. 

What’s one thing you’d want to tell anyone interested in joining CausaLens?

causaLens is a fast-moving company developing cutting-edge technology with many opportunities! There are numerous ways to contribute and to grow personally and professionally.

Your greatest achievement in your career to date?

The transition from theoretical physics to Data Science required a lot of learning but I had a great sense of achievement once I gained enough knowledge and practical experience to become confident in my role.

What emerging tech/tech trends are you most fascinated by right now?

I am very excited about a broad range of applications of AI and the way it affects people in everyday life as well as in the areas of research related to explainability in AI aimed at understanding the underlying processes and its outputs.

 

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