Fernando G.

• Designed and developed the data ETL pipeline to compute massive financial datasets, reducing computation time from several days to hours (PySpark - Databricks - Azure);
• Created a ”methodology on code” to guide new Machine Learning projects development, from which two projects were structured;
• Developed Machine Learning Models for forecasting Bitcoin Trading Volume (XGBoost, 65% accuracy) and Crypto Volatility (Temporal Fusion Transformer, 95% accuracy);
• Developed an MLOps pipeline to put models to production: calculation of hundreds of features, streaming inferences in real-time using FastAPI Websocket server, model monitoring using SQL database and Slack API (Docker on ECS, AWS FeatureStore, FastAPI, SQL, Slack), with an uptime of over 99%;
• Created a numerical Index to score Time Series data and help build trading strategies (statistical correlations, co-integration, ACE score, time series analysis).

Performed customer churn statistical analysis for a large Brazilian B2C video streaming app.

• Developed algorithms for GPS filtering, correction, and performance analysis for amateur football players, including lite football athletes.
• Took charge of ensuring the quality of data products and managing tasks such as backlog, OKRs, and metrics.
• Integrated Python algorithms with AWS on the Serverless Framework, including Lambda, S3, EC2, DynamoDB, and CloudWatch.
• Carried out tasks such as data analysis, implementing Kalman Filters, Neural Networks, Fuzzy Filters, scientific algorithms, statistical insights engine, basic computer vision, and facial recognition algorithms.
• Utilized Python to create powerful data visualizations.
• Maintained the Python infrastructure for the automatic delivery of hundreds of daily.

• Data Scientist from Day 1 of the company, from 0 to over 50000 users

• Algorithms for GPS filtering and correction and performance analysis fore lite football athletes and amateur players

• Client-facing customer success role regarding data services of the product

-Responsible for data products quality assurance, backlog, OKRs, and metrics

• Integrated Python algorithms with AWS on Serverless Framework (Lambda, S3 , EC2, DynamoDB, CloudWatch)

-Tasks included Data Analysis , Kalman Filters , Neural Networks , Fuzzy Filters , scientific algorithms , statistical insights engine ,basic computer vision and facial recognition algorithms

• Powerful data visualization with Python

• Maintenance of Python infrastructure for automatic delivery of hundreds of reports daily