Preparing databases, exploratory and descriptive analyses, and managing all stages of data modelling using Databricks environment.Following central bank guidelines and regulations to implement provision models. Building models to predict the Probability of default, Exposure of Default and Loss Given Default to estimate the Expected loss and fulfilment of BACEN 4966/21 and IFRS9.Monitoring performance, inputs and outputs of Behaviour Scoring models in production.Collaborating with cross-functional teams, including data engineering and data science and accounting, to ensure successful project delivery.

Supported the team by providing business intelligence, statistical analysis, and data leveraging strategy in support of the development, implementation and refinement of regulatory and compliance policies, programs, processes, and tools; conveyed requirements and limitations to non-technical teams. Identified AI/ML opportunities to manage risk, detected anomalous behaviour, forecast/predict outcomes and improved compliance-related business processes.Applied statistical methods to validate and optimise models.Conducted extensive research and worked alongside the Data Engineering team, Fraud & Compliance teams to create digital and automated solutions.Developed models and automation processes through AWS.✔Achievements: Led multiple projects simultaneously and ensured deadlines and results were met. Created the entire Risk & Compliance automated processes from scratch participating in their digital transformation project.Successfully worked in a Start-up environment, being the first Data Scientist to participate in their digital transformation project.

Mapped out client behaviours and interactions with the application, identifying pain points and opportunities. Analysed data through SQL and Google Big Query, created machine learning models utilising Python, created proposals for the commercial team, performed A/B testing, and launched the new feature.Presented insights and recommendations to clients in a clear and concise manner.Collaborated with cross-functional teams following agile methodologies using Jira and version control systems using GIT.✔Achievements:Created a Market Qualified Lead (MQL) model, delivering 28 % improvement in precision in identifying our future paying customers. Implemented an automated process for Lead Generation, which significantly increased the number of captured leads and, therefore, sales results. Implemented a news recommender system, which improved the customer experience.

Defined business goals in cooperation with a team of credit analysts and translated them to machine learning problems. Developed and created predictive models associated with fraud detection and prediction of suspicious activities.Performed Data analytics related to all credit and collection processes (Python, R, SPSS)Developed and created predictive models associated with Credit scoring, Behavior scoring and Collection Scoring (Python, R, SPSS).Constructed executive-level presentations and presented complex analytical findings in a clear, concise, and decision-impacting manner.Collaborated with cross-functional teams, including data engineering and data science, and UX, to ensure successful project delivery.Extraction, transformation, cleaning, and modelling of data using SQL and Pyspark.Monitored performance, inputs and outputs of Behaviour Scoring, Credit Scoring and Collect Scoring models in production.✔Achievements:Implemented models of Credit Scoring for 2 important B2B financial companies in Brazil, increasing approval and reducing default.Implemented models of Credit Scoring for an important retail company in Brazil, increasing approval and reducing default considering the clients that use the app.Developed predictive models associated with Churn detection of students in a big educational group in Brazil and helped the process to optimise the retention of students in the universities belonging to the group.Developed models to predict theft of electricity in an electric utility in Brazil and help to optimise the routes in the special process.Implemented models of Collection Scoring for an important B2C client in Brazil, helping the company to optimise the collection politician in the company.Promoted to Data Scientist - Mid Level after two years working as a Data Scientist - Junior.