Senior Assistant Vice President

Job Title: Machine Learning Modeler Lead – Banking & Financial Services

Location

Department

Employment

Job Summary -

Key Roles and Responsibilities

• Minimum Years of Experience: 8+ years in Retail banking
• Deep business expertise of the different business lines in the Retail banking Credit risk space across the customer lifecycle. Good hands-on knowledge of Banking strategies around Underwriting, Collection & Recoveries,High-Risk Account Management (HRAM) and ECM Strategies.
• Knowledge of Fraud strategy and analytics in Retail banking domain an added advantage.
• Possesses end-to-end hands-on ML solution development experience, from data exploration, feature engineering, model development to validation, deployment and monitoring.
• Develop robust models to solve across the spectrum business problems and drive business benefits. Support and review junior data scientists’ submissions and share enhancement suggestions
• Responsible for documentation / reviews, model reviews and submission
• Responsible for managing queries raised by the model validation teams and defending the model/solution against internal MRM/audit teams.
• 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 and MLOps
• Continuously evaluate new tools, technologies, and frameworks relevant to ML in finance.
• Publish internal research and promote a culture of innovation and experimentation.
  

Qualifications

Education

• Master’s or Similar in Computer Science, Data Science, Statistics, Applied Mathematics, or a related quantitative field.
  

Experience

• Strong business knowledge of banking analytics across the retail banking customer lifecycle.
• 8+ 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 underwriting, collections, ECM, fraud and ethical considerations in banking ML models.
  

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.
  

About The Company