I am a Chemical/Environmental Engineer by trade, and for almost 10 years I did research on how to improve water treatment methods.
I am passionate about Data, programming and reading.
I am currently working at Credit Suisse as a Data Scientist for WM, analysing client and transaction data, and help driving an AML regulatory project on periodic client reviews.
Learning is my biggest passion and what motivates me the most! I am currently completing the Data Engineering Track at DataCamp and also trying to improve on my Visualizations and DevOps skills.
I am interested in discussing anything regarding Data and ML/Deep Learning - drop me an email using the contact form below!
Responsible for the Data & rule-based algorithm design themes on a regulatory driven AML project for periodic client reviews. Main tasks were: - Define sensible analysis parameters, perform root cause analysis of identified issues or risks; - Obtain, validate and prepare accurate data to be presented at Senior Management (SM) committees (Managing Director level). Serve as a technical specialist on the aforementioned meetings - Drive change and enhancements to the current tool, collecting user and SM feedback, and “translating” it to the technical implementation teams, in a quasi Requirements Engineer role
Modeled data to surface populations of clients that pose risk to the bank. Insights from analyses were presented to stakeholders across multiple divisions, with the ultimate goal of reducing regulatory risk. Mains tasks were: - Pipeline development using PySpark and Palantir software - Ad hoc analytics
Operating with renewable assets (Wind/Solar) data, the objective is to provide actionable insights to the costumer that can ultimately be turned into productivity gains.
Business Development & Procurement (both with Hospital Administration and other service providers), directly manage a team of 14 Site Managers, supervise all operations in 12 cities across the country.
Business Development and Procurement - I was also responsible for designing an innovative logistic process, that allowed the company to save ca. 0.3M€/year in custom fees.
Main skills acquired: Python, R, Data Munging, Statistics, Machine Learning, Data Visualization, Natural Language Processing, Deep Learning, Capstone Project