About the Role The candidate will be responsible for the end-to-end implementation of machine learning models and solutions, collaborating with business partners, data scientists, IT teams, and the Model Governance team. Responsibilities • Collaborate with Business Partners, data scientists, IT teams, and Model Governance team to complete end-to-end implementation. • Create, test, and fine-tune machine learning models. • In-depth knowledge of machine learning data structures and modeling, software architecture, libraries, and frameworks. • Strong knowledge of machine learning packages and frameworks. • Data preparation and analysis skills to work with large datasets, preprocess them, and extract features. • Knowledge of data analysis, statistical modeling, and machine learning algorithms. • Compare model outputs using statistical validation techniques. • Build pipelines with Airflow, implement MLOps best practices, optimize, and deploy various ML models and solutions. • Monitor model performance and document results. • Experience with multi-threading and parallelization to improve compute efficiency. Qualifications • Bachelor's Degree in Computer Science or related degree. • 4+ years’ experience with Python programming and Databricks. • 3+ years’ experience with SQL-based queries, including creation, optimization, and integration. • 3+ years’ experience with Data Science and NLP. • Understanding of precision and recall in data science. • Working at an insurance company. • Experience with Data Mining. • Familiarity with clustering and linking of data. • Exposure to Text Mining and Natural Language Processing (NLP). Preferred Skills • Utilize grid space to retain transcripts, enabling keyword search and advanced data retrieval functionalities. • Conduct thorough quality checks to ensure the accuracy and reliability of the data and processes. • Develop saturated language-native queries and build sift SQL based on typing in keywords for enhanced search capabilities. • Create SQL queries outside of the grid space and seamlessly integrate them into our existing systems. • Work with sentence transformers to help review the standard quality on calls from the call centers. • Employ sift technology to enhance our data processing capabilities. • Code and implement solutions primarily in Python, ensuring optimal performance and scalability.