AddressVice President - Data Scientist - Financial Crime
What you will need to succeed in the role:
- Proven experience as a Data Scientist or similar role with 13+ years of experience focused primarily on the Credit/Markets/Financial Crime Risk of a bank/NBFC or Fintech or any other Retail business.
- Post Graduation in Statistics or Mathematics or Economics or Computer Science; any certification on Artificial intelligence courses will be preferred.
- Analytical expertise with AI/ML applications in banking or related industries
- Strong technical knowledge of common AI applications including strong understanding of risks with AI/ML across various risk stripes along with mitigant.
- Strong written and oral communication with ability to clearly communicate complex analytical concepts clearly to management and broad audience.
- Experience in developing and/or validating quantitative models in AI.
- Experience of implementing multiple AI/ML projects end to end from conceptualization to implementation.
- Knowledge of data cleaning, feature engineering, and data normalization techniques is important for preparing the data before feeding it into the models.
- Strong technical understanding of data science, machine learning, analytical methodologies, and tools with hands on experience of when to use what and adapting to the regulatory requirements and guidelines.
- Track record in leading large, complex data science and analytics projects (preferably in the domain of Credit /Markets/Financial Crime Risk and regulatory models) which have provided significant business impacts or regulatory approvals.
- Sound understanding and hands on experience of Large Language Models (LLM) such as Generative AI and Predictive modelling (Linear Regression, Logistic Regression, Decision Tree, Random Forest, etc.) and statistical analysis (Variable Reduction, Feature engineering) with Supervised and Unsupervised machine learning algorithms.
- Preferably well versed with updated current trends in the credit risk regulatory landscape, AI/ML techniques and methods used in model risk management.
- Preferably well versed with Regulatory guidelines on AI/ML Risk Management (EBA, Federal Reserve etc.)
- Hands on knowledge on SAS, Python, PySpark, R and SQL is a must.
