Data Analytics professional with a strong research background and experience in Astrophysics, Big Data and statistics.
Passionate about data, with intense scientific curiosity and determined at solving problems, I hypothesize and test multiple scenarios to find the right solution. In my work I constantly apply the "out of the box" thinking and the versatility necessary to pull both the product and the developer teams together, keeping focus on the end user value.
I believe in continuous learning and self-development and enjoy sharing my knowledge. I like taking MOOCs (Massive Open Online Courses) about Data Science / Machine Learning to improve my technical and programming skills, both in Python and R.
As Astrophysics researcher my X-ray satellite Data Analysis includes cleansing the satellite raw data, coding bash script for automating the data cleaning and processing pipelines, giving the best fitting statistical interpretations based on theoretical models, performing predictive analytics via regression analysis, time series analysis, applying different methods for pattern recognition, amongst others.
Fields of interest
- Data analysis and visualization
- Machine Learning (classification, regression, clustering, random forest)
- Statistical Methods (time series, regression models, hypothesis testing and confidence intervals)
- Python / iPython Notebooks (Jupyter) / R
Highlights Info
- Currently working at Planet as QA Engineer.
- I worked as Ph.D. candidate in Astrophysics at Leibniz-Institut für Astrophysik Potsdam (AIP) and my field of research was X-ray satellite Data Analysis.
- I specialised in X-ray satellite Data Analysis during my Master's degree Thesis at INAF - IASF Bologna under the supervision of Dr. Paola Grandi.