Machine Learning Engineer/Data Scientist

Overview In this role you will design, build, and sustain end-to-end ML solutions that drive business insights. You’ll work closely with stakeholders and cross-functional teams to translate objectives into deployable models, pipelines, and production-ready tooling. You will own model lifecycle, from feature engineering to monitoring and retraining, enabling scalable, data-driven decision making. The opportunity offers hands-on ML, MLOps, and impact at scale in a software-focused environment. ResponsibilitiesCollaborate with stakeholders to translate business needs into ML solutionsDevelop end-to-end ML models including training, evaluation, and selectionDeploy and monitor models in production with retraining strategiesDesign and maintain scalable data pipelines for data processingApply modeling techniques including logistic regression, random forest, XGBoost, and neural networksUtilize MLOps platforms (e.g., MLflow) to manage model lifecycleWork with SQL, Pandas, Spark, and Snowflake for data manipulation and analyticsUse Python and ML/DL libraries (scikit-learn, TensorFlow, Keras, PyTorch) to build and test modelsGenerate reporting and insights using SQL and data toolsExperience with SAS is a plus Key requirementsBachelor’s or Master’s degree in Computer Science, Data Science, Statistics, or a related fieldProven experience in machine learning model development and deploymentStrong programming skills in Python and familiarity with modern ML frameworksExperience working with cloud-based or distributed data systems is a plusExcellent problem-solving skills and ability to work independently and in a teamproblem-solvingindependence with teamworkcollaboration and communicationPythonscikit-learnTensorFlow