A. Summary:
Responsible for designing and managing the enterprise data architecture that supports banking operations, analytics, and regulatory compliance. This role ensures that data systems are scalable, secure, and aligned with business strategy
B. Key Responsibilities:
- Analyze large volumes of structured and unstructured banking data to identify trends, patterns, and opportunities.
- Develop and deploy machine learning models for use cases such as credit scoring, fraud detection, customer segmentation, and churn prediction.
- Collaborate with business units to translate complex data findings into actionable insights and strategies.
- Design and maintain dashboards and reporting tools to monitor key performance indicators (KPIs).
- Ensure data quality, integrity, and compliance with banking regulations and data governance policies.
- Work with IT and data engineering teams to optimize data pipelines and infrastructure.
- Stay updated on the latest data science techniques, tools, and regulatory changes in the financial sector.
C. Qualifications:
- Bachelor's or Master's degree in Data Science, Computer Science, Statistics, Mathematics, or related field.
- 3+ years of experience in data science or advanced analytics, preferably in banking or financial services.
- Proficiency in programming languages such as Python, R, or SQL.
- Experience with machine learning frameworks (e.g., scikit-learn, TensorFlow, XGBoost).
- Strong knowledge of data visualization tools (e.g., Power BI, Tableau).
- Familiarity with banking products, customer behavior, and regulatory requirements.
D. Preferred Skills:
- Experience with big data platforms (e.g., Hadoop, Spark).
- Knowledge of financial risk modeling, credit scoring, or anti-money laundering (AML) analytics.
- Understanding of cloud platforms (e.g., AWS, Azure, GCP).
- Certification in data science or analytics (e.g., Microsoft Certified Data Scientist, Google Data Engineer).
