Design, build, and deploy production‑grade ML and AI models, including credit risk scorecards, fraud detection, customer lifetime value, and liquidity forecasting models.
Lead model validation activities such as back‑testing, stress testing, and ongoing performance reviews in line with the bank's Model Risk Management (MRM) framework.
Champion MLOps best practices, including CI/CD pipelines, experiment tracking, model monitoring, and data/model drift detection.
Oversee end‑to‑end model deployment and integration with core banking systems and digital platforms.
Provide technical leadership, peer reviews, and mentorship to junior and mid‑level data scientists, while enforcing coding, documentation, and model development standards.
Contribute to team capability building through knowledge‑sharing sessions, internal training, hackathons, and participation in talent recruitment and onboarding.
Ensure all models and analytical outputs comply with banking regulations, ethical AI principles, and regulatory expectations, including preparation of documentation for internal and external reviews.
Act as a strategic data science partner to business domains, translating complex cross‑functional problems into scalable analytics solutions and driving enterprise‑wide adoption of data‑driven decision‑making.
Role Requirements:
Bachelor's or Master's degree in Data Science, Statistics, Computer Science, Mathematics, Engineering, or a related quantitative discipline, with proven experience in data science, quantitative analytics, or similar roles.
Strong programming proficiency in Python and/or R, with deep expertise in machine learning, deep learning, NLP, time‑series forecasting, and causal inference.
Proven MLOps experience, including MLflow or Weights & Biases, CI/CD pipelines, model monitoring, and model registry management.
Hands‑on experience with cloud ML platforms (AWS SageMaker, Azure ML, or GCP Vertex AI) and large‑scale data processing frameworks such as Spark, Databricks, or BigQuery.
Advanced SQL skills and experience working with complex, large‑scale enterprise data environments, preferably within banking or regulated industries.
Ability to translate ambiguous business needs into well‑defined technical specifications, data structures, and scalable analytics solutions, with strong investigative and problem‑solving skills.
Strong communication skills, with the ability to clearly explain technical concepts to non‑technical stakeholders and collaborate effectively across business and technology teams.
Results‑oriented, adaptable, and able to perform under pressure in a deadline‑driven environment, with high proficiency in MS Office and a solid understanding of data sensitivity and compliance requirements.
