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Schedule Interview NowMy name is Daniele M. and I have over 1 years of experience in the tech industry. I specialize in the following technologies: Machine Learning, Artificial Intelligence, Quantitative Analysis, Quantitative Finance, Research & Development, etc.. I hold a degree in Master of Science (MS), Doctor of Philosophy (PhD). Some of the notable projects I’ve worked on include: Interactive Brokers Data Management System, Breakout / resistance trading with an end-to-end ML approach, Developing a cloud data pipeline to analyze terabytes of data.. I am based in Essen, Germany. I've successfully completed 3 projects while developing at Softaims.
I'm committed to continuous learning, always striving to stay current with the latest industry trends and technical methodologies. My work is driven by a genuine passion for solving complex, real-world challenges through creative and highly effective solutions. Through close collaboration with cross-functional teams, I've consistently helped businesses optimize critical processes, significantly improve user experiences, and build robust, scalable systems designed to last.
My professional philosophy is truly holistic: the goal isn't just to execute a task, but to deeply understand the project's broader business context. I place a high priority on user-centered design, maintaining rigorous quality standards, and directly achieving business goals—ensuring the solutions I build are technically sound and perfectly aligned with the client's vision. This rigorous approach is a hallmark of the development standards at Softaims.
Ultimately, my focus is on delivering measurable impact. I aim to contribute to impactful projects that directly help organizations grow and thrive in today’s highly competitive landscape. I look forward to continuing to drive success for clients as a key professional at Softaims.
Main technologies
1 year
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Potentially possible
Siemens
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