A Logistic Regression developer designs, implements, and maintains predictive models based on logistic regression, a statistical method used for binary classification problems. They analyze data sets, select appropriate features, and apply logistic regression algorithms to predict outcomes. They validate models, interpret results, and adjust parameters to optimize performance. They use programming languages like Python or R and tools like TensorFlow or Scikit-learn. They collaborate with data scientists, data engineers, and other stakeholders to integrate these models into larger systems, ensuring they meet business needs. They also continuously monitor model performance and refine them as necessary.