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.

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.

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).