Daniel Sganzerla

Background

About Daniel Sganzerla

I am a Data Professional with a BSc in Statistics and a postgraduate degree in Data Science, with solid experience in data analysis, predictive modeling, and team leadership. Over the past years, I have led analytical projects across healthcare, sports, and industrial sectors, including simple but effective analyses, process automation, and advanced statistical modeling with machine learning. As a technical lead and consultant, I have been responsible for designing and implementing predictive algorithms published in international journals, structuring data science departments, and automating decision-making processes using AI. My work has generated measurable impact, including more than 20% process automation, significant reduction of fraud-related waste, and over 40 scientific publications. One of my greatest challenges has been combining technical depth and continuous learning with strong interpersonal and communication skills. Over the years, I have worked with SQL, R, and Python, and gained practical experience with Qlik, Power BI, and REDCap. I apply best practices such as the CRISP DM methodology and research frameworks including TRIPOD, STROBE, and CONSORT. I am passionate about using data to answer meaningful questions, particularly in healthcare, sports performance, and industry. I believe that technology and AI are powerful partners, not substitutes. While AI accelerates processes, human reasoning, creativity, and ethical judgment remain at the heart of every real solution.