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Schedule Interview NowAt Softaims, I’ve found a workplace that thrives on collaboration and purposeful creation. The work we do here is about more than technology—it’s about transforming ideas into results that matter. Every project brings a mix of challenges and opportunities, and I approach them with a mindset of continuous learning and improvement. My philosophy centers around three principles: clarity, sustainability, and impact. Clarity means designing systems that are understandable, adaptable, and easy to maintain. Sustainability is about building with the future in mind, ensuring that the work we do today can evolve gracefully over time. And impact means creating something that genuinely improves how people work, connect, or experience the world. One of the most rewarding aspects of working at Softaims is the diversity of thought that every team member brings. We share insights, question assumptions, and push each other to think differently. It’s this culture of curiosity and openness that drives the quality of what we produce. Every solution we deliver is a reflection of that shared dedication. I’m proud to contribute to projects that not only meet client expectations but also exceed them through thoughtful execution and attention to detail. As I continue to grow in this journey, I remain focused on delivering meaningful outcomes that align technology with purpose.
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This project provides tools to download historical market data from Interactive Brokers (IB) at various timeframes and efficiently store it in a structured format. It also includes utilities for discovering available contracts across different exchanges and a web interface for exploring market data.
Trading technical signals of stock prices poses many challenges for which a clearly defined data-driven answer does not exists. The most evident is the very existence of patterns with any predictive power whatsoever. To experiment with a data-driven approach, we designed a deep neural network (DNN) architecture that treats the problem as an end-to-end prediction task, where the only human input is in setting the hyper-parameters that regulate how prediction losses are computed. Machine Learning, Python, TensorFlow, Keras, SQL, Trading, Technical Analysis, R&D
Working for a German startup, I lead 4 tech teams to develop a big-data analytics platform. The backbone of the product was a big data analytics pipeline capable of ingesting GB's of data every data and running tens of different analytics models. The business goal was to provide novel and accurate insights to thousands of IoT devices across 5 continents in a low-latency, continuous fashion. Because of the specific nature of the data produced at the source, we built most software in-house, and we R&D'ed tens of ML models for data characterization and prediction.
Master of Science (MS) in Physics
2002-01-01-2008-01-01
Doctor of Philosophy (PhD) in Physics
2009-01-01-2012-01-01