Applied Researcher / Machine Learning Consultant

We are looking for an experienced researcher, data scientist, or machine learning engineer to help design and document a mathematical framework for combining multiple sources of evidence into calibrated confidence scores for a classification system. The project involves: Designing probabilistic and statistical scoring frameworks Working with heterogeneous signals and model outputs Reasoning about uncertainty, calibration, and confidence estimation Producing clear mathematical documentation and worked examples Evaluating trade-offs between accuracy, speed, and computational cost Ideal background: Machine Learning Statistics Applied Mathematics Information Retrieval Entity Resolution / Classification Systems Probabilistic Modeling This is a short-term consulting engagement with a well-defined deliverable.