Data Science Engineer

As a Data Science Engineer with 6+ years of experience, your role will involve designing and developing machine learning models for various business problems. You will be responsible for implementing data preprocessing, feature engineering, and model training using Python and Databricks. Additionally, you will work on model deployment, monitoring, and scaling in cloud or on-premise environments. Collaborating with cross-functional teams to ensure model alignment with business needs and optimizing data pipelines to support model building and execution will also be part of your responsibilities. Ensuring the scalability, performance, and reliability of deployed models and implementing MLOps practices for automated testing, continuous integration, and model versioning will be essential tasks. Monitoring model performance in production and refining models as needed will also fall under your responsibilities. Key Responsibilities: - Design and develop machine learning models for various business problems. - Implement data preprocessing, feature engineering, and model training in Python and Databricks. - Work on model deployment, monitoring, and scaling in cloud or on-premise environments. - Develop and optimize data pipelines to support model building and execution. - Collaborate with cross-functional teams to ensure model alignment with business needs. - Ensure the scalability, performance, and reliability of deployed models. - Implement MLOps practices for automated testing, continuous integration, and model versioning. - Monitor model performance in production and refine models as needed. Qualifications Required: - 6+ years of experience in Data Science, Machine Learning, and Model Deployment. - Strong expertise in Python, Databricks, and machine learning frameworks (e.g., scikit-learn, TensorFlow, PyTorch). - Experience with data pipeline development and cloud platforms. - Knowledge of MLOps practices for scalable and automated model deployment. - Strong SQL skills and experience working with large datasets. - Familiarity with version control systems (e.g., Git) and containerization (e.g., Docker). - Ability to work in a UK shift and collaborate with global teams. (Note: The additional details of the company were not present in the provided job description) As a Data Science Engineer with 6+ years of experience, your role will involve designing and developing machine learning models for various business problems. You will be responsible for implementing data preprocessing, feature engineering, and model training using Python and Databricks. Additionally, you will work on model deployment, monitoring, and scaling in cloud or on-premise environments. Collaborating with cross-functional teams to ensure model alignment with business needs and optimizing data pipelines to support model building and execution will also be part of your responsibilities. Ensuring the scalability, performance, and reliability of deployed models and implementing MLOps practices for automated testing, continuous integration, and model versioning will be essential tasks. Monitoring model performance in production and refining models as needed will also fall under your responsibilities. Key Responsibilities: - Design and develop machine learning models for various business problems. - Implement data preprocessing, feature engineering, and model training in Python and Databricks. - Work on model deployment, monitoring, and scaling in cloud or on-premise environments. - Develop and optimize data pipelines to support model building and execution. - Collaborate with cross-functional teams to ensure model alignment with business needs. - Ensure the scalability, performance, and reliability of deployed models. - Implement MLOps practices for automated testing, continuous integration, and model versioning. - Monitor model performance in production and refine models as needed. Qualifications Required: - 6+ years of experience in Data Science, Machine Learning, and Model Deployment. - Strong expertise in Python, Databricks, and machine learning frameworks (e.g., scikit-learn, TensorFlow, PyTorch). - Experience with data pipeline development and cloud platforms. - Knowledge of MLOps practices for scalable and automated model deployment. - Strong SQL skills and experience working with large datasets. - Familiarity with version control systems (e.g., Git) and containerization (e.g., Docker). - Ability to work in a UK shift and collaborate with global teams. (Note: The additional details of the company were not present in the provided job description)