Data Scientist

We are seeking a detail-oriented and analytical Data Scientist / Data Analyst to support data-driven analysis. The ideal candidate will analyze large datasets, develop predictive models, generate actionable insights, and create dashboards that improve business performance, customer experience, risk management, and operational efficiency.

• Key ResponsibilitiesCollect, clean, validate, and analyze structured and unstructured banking data.
• Develop dashboards and reports to monitor business KPIs.
• Perform customer segmentation, profitability analysis, and behavioral analytics.
• Build predictive models for customer churn, loan default, fraud detection, and cross-selling opportunities.
• Analyze transaction patterns to identify anomalies and business opportunities.
• Collaborate with business teams including Retail Banking, Credit, Risk, Operations, and Compliance.
• Automate recurring reports and data pipelines.
• Conduct statistical analysis and hypothesis testing.
• Present analytical findings and recommendations to stakeholders.
• Ensure data quality, governance, and regulatory compliance.
• Support digital transformation and AI initiatives within the bank.
• Required QualificationsBachelor's or Master's degree in Data Science, Statistics, Mathematics, Computer Science, Economics, Finance, or a related field.
• 2–5 years of experience in data analytics, business analytics, or data science (banking or financial services preferred).
• Strong analytical and problem-solving skills.
• Excellent communication and presentation skills.
• Technical SkillsRequiredSQL (Advanced)
• Python (Pandas, NumPy, Scikit-learn)
• R (preferred)
• Advanced Microsoft Excel
• Power BI or Tableau
• Statistical analysis and predictive modeling
• Data visualization
• ETL concepts
• Data cleaning and preprocessing
• PreferredMachine Learning
• Time Series Forecasting
• Natural Language Processing (NLP)
• Cloud platforms (AWS, Azure, or GCP)
• Big Data tools (Spark, Hadoop)
• Git version control

Finance Domain Knowledge Experience in one or more of the following areas is desirable:

• Retail Banking
• Corporate Banking
• Credit Risk Analytics
• Fraud Analytics
• Anti-Money Laundering (AML)
• Customer Lifetime Value (CLV)
• Loan Portfolio Analysis
• Basel regulations
• Regulatory reporting
• Financial products and banking operations
• Key CompetenciesCritical thinking
• Business acumen
• Data storytelling
• Stakeholder management
• Attention to detail
• Problem-solving
• Team collaboration
• Time management
• Key Performance Indicators (KPIs)Accuracy and quality of analytical reports
• Timely delivery of dashboards and insights
• Improvement in business KPIs through analytics
• Model performance (accuracy, precision, recall, AUC, etc.)
• Automation of manual reporting processes
• Stakeholder satisfaction
• Compliance with data governance standards
• Preferred CertificationsMicrosoft Power BI Data Analyst
• Google Data Analytics Professional Certificate
• SAS Certified Data Scientist
• AWS Certified Machine Learning – Specialty
• Azure Data Scientist Associate
• Nice-to-Have ExperienceBanking analytics
• Credit scoring models
• Fraud detection models
• Customer segmentation
• Marketing analytics
• Risk analytics
• Financial forecasting
• SQL optimization
• Dashboard development
• Machine learning model deployment