Data Scientist with several years of academic and industrial experience researching and developing machine learning algorithms and out-of-the-box solutions for real world applications. Expertise in public speaking and communicating ideas to diverse teams and audiences.

I love machine learning, climbing, and baking. All three are about understanding the tiny little details, to be able to design a creative solution to a specific problem.

I love working with people, especially with a diverse set of people that have different life experiences. That is why for me a workplace is not just somewhere where you work a certain number of hours, but where you create a culture, a team, a space where you can inspire and count on each other. I believe everyone has something valuable to bring to the table. That’s why I joined the advisory board of Women in Tech for Meela. To empower and work with other women to build something awe-inspiring.

On the technical side, I am passionate about working with algorithms. Every machine learning algorithm is different, it shapes the data in such a different way, that when we get an intuition of what’s going on in the background, it seems like magic. But it’s not, it’s different mathematical blocks working together. When I research and develop algorithms, I always have an efficiency focus in mind. Efficiency in terms of energy (to use less power and be more sustainable), efficiency in terms of speed (so that it runs faster) and efficiency in terms of memory capacity (so that the algorithm can run on any type of device, big or, my favorite, tiny).

I spent several years mastering that during my PhD studies and while working as a data scientist. During that time I gained experience in:

Programming — Data analysis

• Python, Java, C (in order of proficiency)
• Pandas, numpy, matplotlib, scipy
• Automating experiments and tests using shell/bash scripts

Machine Learning

• Researching and publishing papers on energy efficient algorithms for real-time data analysis
• Decision trees, Neural networks, deep learning, gradient boosting, SVM, PCA
• Scikit-learn, TensorFlow, PyTorch

Not-so-soft skills:

• Communicating ideas to different set of audiences, both internally and at machine learning conferences
• Team building
• Innovation