I am a Senior Data Scientist with extensive experience in developing mathematical models and optimizing trading algorithms, primarily using Python and its scientific libraries such as numpy, pandas, and scikit-learn. My work has directly contributed to increased trading profitability and enhanced data-driven decision-making processes. I have a strong background in statistical analysis and data visualization, having authored comprehensive reports using tools like Tableau and Oracle BusinessObjects.
Throughout my career, I have played a pivotal role in shaping regional government strategies through advanced statistical modeling and demographic forecasting. I am skilled in building and maintaining data pipelines from various databases including MongoDB and PostgreSQL, ensuring data quality and accessibility for strategic planning and policy evaluation.
My expertise extends to algorithm development in R and Python, where I have engineered efficient sampling methods and innovative data cleaning techniques. I have also contributed to academic research, publishing several papers on mortality modeling and policy evaluation in reputable journals and conferences.
In addition to my professional work, I have experience as a contract professor, teaching descriptive statistics, probability calculus, and inferential statistics to university students. I have developed R-Shiny applications to facilitate statistical learning, demonstrating my commitment to education and knowledge sharing.
I am passionate about technical problem-solving, systems thinking, and continuous learning. My interdisciplinary collaboration skills and strong communication abilities enable me to work effectively across teams and mentor others. I am motivated by challenges that require innovative solutions and enjoy engaging with complex data to uncover actionable insights.
I hold a PhD in Circular Economy with a focus on mortality modeling, and I graduated with honors in statistical and actuarial sciences as well as business statistics and computer science. My academic and professional journey reflects a deep commitment to advancing statistical science and applying it to real-world problems.
Development of mathematical models for electricity production and load. Development and optimization of trading algorithms in Python (numpy/Pandas/scikit-learn): increased trading profitability by 100 basis points in first 60 days. Authored comprehensive reports in Tableau, maintained and improved data pipelines from MongoDB and PostgreSQL.
Played a key role in shaping regional government strategy through advanced statistical analysis for strategic planning. Led the construction of data pipelines and authored comprehensive reports in Oracle BusinessObjects, showcasing expertise in data visualization and reporting. Engineered algorithms in R for efficient sampling, data analysis, and model building, demonstrating a data-driven approach to decision-making. Published groundbreaking research on policy evaluation modeling using advanced statistical methods (Springer Genus, 2023). Developed demographic forecasting models and implemented innovative data cleaning methods for improved data quality. Designed and implemented robust sampling schemes, supporting data collection, analysis, and internal audit procedures.
Created a team ranking system for the online football manager game Hattrick.org. Developed a customized rating system based on ELO ratings, tailored to the unique context of the game. Performed calculation of approximate expected values of nested stochastic processes, integrating them into the game code. Utilized various data analysis methodologies, including simulation, generalized linear models, regression trees, and custom dissimilarity measures, in Python and R.
Instructed ~40 students per year in descriptive statistics for Statistical and actuarial science students (2008-2019). Instructed 150+ students per year in descriptive statistics, probability calculus and inferential statistics for Economics students (2016-2021). Development of R – Shiny applications to facilitate statistical learning for high school and junior students.
Developed hierarchical Bayesian models in Python, R and C based on matrix factorization (Latent Dirichlet Analysis, an upgrade of PCA) to classify unstructured textured data by topic, cluster analysis (KMeans) by topic and automatic classification by topic. Received the 2007 InnovAction prize for the most innovative companies in the Friuli Venezia Giulia region for the project of a smart search engine on administrative procedures data on which the Master thesis was based.
PhD in Circular Economy
Research area: Mortality modelling
Master of Statistical and Actuarial Sciences
Graduated with 110/110 cum laude
Bachelor of Science in Business Statistics and Computer Sciences
Graduated with 110/110 cum laude
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