Data Scientist

Developed a Data Visualization tool using Python and Streamlit to analyze magnetic signals obtained from several types of magnetometers (National Instruments, Oros, Meda, Narda) in order to detect electromagnetic interference (EMI).

• Created interactive graphs depicting amplitude versus time, filtered time, and amplitude versus frequency (FFT) using Plotly, facilitating in-depth signal analysis.

• Engineered AC/DC digital filters to reduce noise, optimizing the accuracy of EMI detection using Scipy.

• Implemented a decimation process to effectively manage large EM signals.

• Performed signal processing analysis using Pandas and Numpy.

• Collaborated with technical team using GitHub to improve a defect detection system designed for manufacturing customers.

• Implemented microservices using FastAPI, Flask, and gRPC to process large (10k x 8k pixel) images and apply them into deep learning models.

• Created Python package that utilized a third-party API to streamline the annotation process.

• Implemented unit and integration tests using Docker and Python to improve the quality of delivered code.

• The research aimed at combining tensor techniques with time series forecasting for route prediction of suspect vehicles using sensor data.

The goal of the project was to develop a system for automating processes at a Big Tech from US using machine learning techniques. Specifically, the system was designed to help managers to give access to internal applications and optimise the response time for it.

• Creating a recommendation engine using machine learning models with a rejection option over highly imbalanced datasets. Data visualisation, python programming, data cleaning/processing, feature engineering and selection, model training and evaluation, data analysis and data ETL using python;
• Performed feature engineering using high imbalanced datasets from various data sources (AWS S3, PostgresQL, MySQL, Cassandra);
• Handling the full data science cycle, from feature engineering to model deployment;
• Build a recommendation system to assist upper management with virtual assets access control decision making.
• Creating, evaluating, deploying and maintaining machine learning models as web services;
• Implemented techniques to optimize models such as: feature engineering and selection, redundancy detection, outlier detection, over and under sampling, model calibration and dataset drift detection;
• Designed data pipelines using Python to deal with financial data and migrate date between systems;
• Communicated results and impacts of the models to stakeholders and relevant parties through reports and dashboards.