Data Scientist ML Engineer

Key Responsibilities:

• Credit & Risk Analytics: Design, develop, and optimize ML models for credit scoring, risk prediction, and scorecard generation.
• Model Deployment & Automation: Implement scalable pipelines for model training, validation, and deployment in production environments.
• Feature Engineering: Identify, extract, and engineer key features from structured and unstructured data to enhance model performance.
• Model Monitoring: Establish continuous monitoring frameworks to track model drift, performance metrics, and data quality.
• Research & Innovation: Explore and apply state-of-the-art ML and transformer architectures to improve predictive accuracy and interpretability.
• Collaboration: Work closely with data engineers, product managers, and domain experts to translate business objectives into robust ML solutions.

Required Skills and Experience:

• Machine Learning: 3+ years of hands-on experience in developing, training, and deploying ML models for structured or tabular data.
• Statistical Modeling: Solid understanding of statistical concepts, feature engineering, and model evaluation techniques.
• ML Frameworks: Experience with scikit-learn, PyTorch, or TensorFlow for building and optimizing predictive models.
• Python Programming: Strong proficiency in Python, with experience using NumPy, Pandas, and Matplotlib for data manipulation and analysis.
• Data Handling: Practical experience with large datasets, data cleaning, preprocessing, and transformation for ML workflows.
• SQL & APIs: Proficiency in writing SQL queries and integrating ML models with APIs or backend systems.
• Version Control & Collaboration: Familiarity with Git and collaborative model development practices.
• Analytical Thinking: Strong problem-solving skills with the ability to translate business problems into data-driven ML solutions.