Data engineer / Machine learning to build event matching system across prediction markets

We are looking for a specialist to help develop a methodology or tool for matching identical events across different platforms (e.g., Kalshi and Polymarket). The data includes a list of events with different names, tags, and unique IDs that do not match across sources. The goal is to identify which records refer to the same real-world event despite differences in wording and data structure. It is important to ensure high matching accuracy. Design and implement an intelligent matching system for cross-platform event data Apply NLP (Natural Language Processing) techniques to analyze and compare event names and descriptions Develop similarity models (e.g., fuzzy matching, embeddings, semantic similarity) Handle inconsistencies in tags, categories, and IDs across datasets Ensure high accuracy and reliability in event matching Optimize and scale the solution for larger datasets Strong experience in Machine Learning, AI, and Data Science Expertise in NLP techniques (text similarity, embeddings, transformers, etc.) Experience with Python (pandas, scikit-learn, NLP libraries) Familiarity with data matching / entity resolution / record linkage Experience working with APIs and structured/unstructured data

Project ID: 40371029