Matthias is an actuary with over six years of experience in machine learning. He was the chief data scientist in a multinational company—leading AI projects in eight countries. The types of projects that Matthias are looking for would ideally involve deep learning, analytics, and data-related tasks.
Delivering data warehouse and ETL solutions as part of an Agile team using advanced ML techniques to improve performance and processes.
Helping build and improve infrastructure, application, and performance development and ensuring tight security including data encryption, security groups, and environment scanning.
Ensuring high-quality deliverables and implementing DevOps and security best practices in fast-paced environments.
Backtested and implemented market-making strategies and created a cross-chain execution engine.
Worked on tokenomics and IDO planning for several projects.
Developed and maintained a protocol for semi-algorithmic stablecoins.
Worked remotely as the acting lead data scientist for a Series C startup based in San Francisco.
Created the core business optimization model for supply matching, which uses a similar architecture as Google's AlphaGo and was written in Python with C++ binding.
Maintained high code quality through code reviews, automated tests, and continuous integration.
Composed several reports and insights to improve supply matching using graph theory, statistic inference, and Machine Learning.
Led the churn-reduction program with an objective of a 20% reduction in churn across eight countries.
Oversaw the development of ML algorithms and management of external resources to design and implement the final architecture.
Created an ML algorithm to improve the company's mobile app by understanding client behavior to remind them of actions they might have forgotten to do.
Supervised the hiring, building, and leading of a team of three data scientists and led a team of five consultants based in Spain.
Developed a financial analysis to justify capital investment in Data Science projects.
Defined and developed a dynamic pricing library for automobile insurance in Chile.
Performed R&D at the data laboratory of the head office in Paris (NLP, Deep Learning, and so on).
Combined Artificial Intelligence and behavioral economics to automate claim payments.
Built a tool to improve quarterly closing, whereby closing time went from one month per quarter to four days per quarter.
Improved a reserve calculation algorithm to not depend on human interactions for predictions.
Automated the back-testing of several finance algorithms for the quick development of solutions.
Consulted with various clients on actuarial science and portfolio analyses.
Segmented a health insurance company portfolio to predict the financial impact of a new regulation and gave recommendations to clients as to what type of product to develop.
Led the yearly update for a product of Addactis PM Export and coordinated and tested the software development with the engineering team.
Gave talks about using Machine Learning to learn about client behavior.
Developed a web scraper for Chilean insurance businesses.
Created a model to react to competitor price changes.
Utilized game theory, behavioral economics, and machine learning for profitability.
Developed a supply matching algorithm using Python and C++ bindings.
Implemented Monte Carlo Tree Search and a fast/slow neural network evaluator.
The algorithm aimed to improve COGS and reduce errors through optimal moves.
Developed a recommender system for an Indian online video-sharing platform.
Recommendations were generated at the end of each video, considering context rather than pure content similarity.
API response time was 45 minutes.
