I am a Machine Learning–focused Data Scientist and current student at Riga Technical University, with a strong interest in designing and deploying scalable, production-ready machine learning systems.

I have hands-on experience working with large-scale financial data through an industry–academia collaboration with SEB Bank, where I built high-throughput data pipelines and applied deep learning models for anomaly detection and predictive modeling within High-Performance Computing (HPC) environments. My work involved transforming high-dimensional raw data into robust feature sets, training and evaluating models, and communicating technically rigorous insights to both academic researchers and industry professionals.

I am actively developing my profile toward Machine Learning Engineering, with a focus on building reliable, interpretable, and scalable ML solutions that perform under real-world constraints. My technical interests include supervised and unsupervised learning, deep learning for time-series and tabular data, feature engineering, model evaluation and optimization, and explainability for high-stakes ML applications.

I am particularly motivated by challenges in financial machine learning, fraud and anomaly detection, and applied AI, where model performance, robustness, and transparency are critical. I value environments that emphasize strong engineering practices, data-driven decision-making, and continuous learning.