Bárbara Santos

I’m a Data Scientist and MSc Data Engineering student focused on turning messy requirements into reliable data products—especially where lifecycle analytics and rule-based decision logic matter. I bring hands-on experience delivering insights across automotive and energy contexts, with a strong bias toward automation and measurable outcomes.

In my curricular internship, I developed a Python tool that accelerated migration from TIBCO BW5 to MuleSoft, enabling automated component conversion. I also advanced my integration and API development work by building and enhancing automotive-sector APIs using AWS while supporting production needs.

On data engineering and analytics initiatives, I’ve worked with Azure, Databricks, SQL, Python, and Power BI to translate stakeholder needs into implementation-ready datasets. I built a vehicle lifecycle analytics framework using Spark/Databricks to track progression from production to retail, and I designed rule-based logic for segmentation, lead times, and sales-date estimation to support operational decision-making.

Beyond day-to-day analytics, I’m actively building depth in applied machine learning and big data systems. My projects include an end-to-end Spark + AWS S3 pipeline with sentiment analysis, aspect-based sentiment, and summarization delivered via a REST API and web interface, and my master’s thesis explores deep learning for pancreatic cyst segmentation and risk classification in CT imaging.