Develop and validate applied machine learning, signal processing, and statistical models using noisy real-world hormone data. Explore datasets, engineer features, select models, design experiments, and conduct error analysis to ensure results are robust and generalizable. Investigate production performance, diagnose failures, identify distribution shifts and biases, and translate findings into model improvements or data-collection strategies. Communicate conclusions clearly to technical and non-technical stakeholders while producing reproducible Python code for eventual production deployment.