Machine Learning Modeling Lead - Credit Modeling

• Lead end-to-end ML solution development & innovation from data exploration, feature engineering, model development, validation, deployment, and monitoring.
• Develop robust models which can drive business benefits. Support and review junior scientist submissions and share enhancement suggestions
• Responsible for documentation/documentation reviews, model reviews and submission
• Responsible for managing queries raised by Validation teams for the model
• Collaborate with implementation teams to deploy models into production environments (cloud or on premises).
• Work closely with business stakeholders to translate banking domain challenges into data-driven solutions.
• Guide junior data scientists and engineers on best practices in model development
• Continuously evaluate new tools, technologies, and frameworks relevant to ML in finance.
• Publish internal research and promote a culture of innovation and experimentation.

• Strong business knowledge of banking analytics across the retail banking customer lifecycle.
• 9+ years of experience in applied machine learning model development in the banking or financial services domain.
• Hands-on experience leading ML projects and teams.
• Strong experience with model development, deployment and monitoring in production environments.
• Familiarity with collections, underwriting, fraud and ethical considerations in banking ML models.
• Demonstrable leadership ability, superior problem solving and people management skills
• Master’s or Similar in Computer Science, Data Science, Statistics, Applied Mathematics, or a related quantitative field

Skills :

• Expert in Python, SQL, ML libraries (Numpy, Pandas, Scikit-learn, TensorFlow, PyTorch) and techniques (Regression, Decision Trees, Ensembles: XGBoost, GBM, Random Forest, Unsupervised Learning, etc.).
• Knowledge of MLOps frameworks (MLflow, Kubeflow, Airflow, Docker, Kubernetes) is added benefit.
• Strong grasp of statistical modeling, optimization, and deep learning techniques.
• Excellent communication skills and ability to explain complex concepts to non-technical stakeholders.