Roles and Responsibilities

• Develop and deploy machine learning models using Python and Databricks, including Unity Catalog Workflows and MLflow.
• Engineer features from raw data to improve model performance through selection, transformation, and construction of meaningful variables.
• Collaborate with cross-functional teams to identify opportunities for AI-driven improvements and innovations.
• Ensure the robustness, scalability, and accuracy of models through rigorous testing, validation, and continuous improvement.
• Conduct data mining and advanced analysis using state-of-the-art methods to uncover insights and trends.
• Cleanse, process, and verify the integrity of data used for analysis.
• Develop analytical models that support high-level strategic decisions.
• Maintain and enhance existing models and analytics solutions.
• Handle ad hoc data analysis and requests from business stakeholders.
• Provide support for ongoing analytical processes and ensure timely delivery of insights.
• Prepare and present findings, visualizations, and recommendations to both technical and non-technical stakeholders.
• Maintain comprehensive documentation of code, methodologies, and workflows.

Required Skills

• Proven experience applying AI/ML/DL methods and algorithms across various business functions.
• Skilled in model optimization, evaluation, and selection techniques.
• Strong Python programming skills.
• Proficient in machine learning libraries such as scikit-learn, TensorFlow, Keras, XGBoost, etc.
• Advanced knowledge of statistics and tools for data analysis (e.g., NumPy, Pandas, Matplotlib).
• Proficient in SQL for complex data extraction, transformation, and analysis (e.g., joins, CTEs, aggregations, and window functions).
• Experience working with machine learning platforms and cloud-based analytics environments.
• Strong organizational and time-management skills; able to handle multiple priorities in a fast-paced setting.
• Excellent communication skills with the ability to explain complex technical concepts to non-technical audiences.

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