We are seeking a motivated Data Scientist – Banking Domain to support analytics, predictive modeling, and data-driven decision-making for banking and financial services initiatives. This role focuses on analyzing customer, account, transaction, payment, lending, and risk-related data to identify trends, build models, and support business insights.
The ideal candidate will work with business, analytics, BI, and technology teams to develop data models, perform statistical analysis, create dashboards, and support machine learning use cases within banking operations.
<> Key Responsibilities
Analyze banking and financial services data including customer, account, transaction, payment, lending, and credit data
Write SQL queries to extract, clean, transform, and validate datasets from multiple sources
Use Python for data analysis, feature engineering, statistical analysis, and basic machine learning models
Support development of predictive models for customer behavior, risk analysis, fraud detection, churn, loan performance, and campaign effectiveness
Perform exploratory data analysis to identify trends, patterns, anomalies, and business insights
Build and validate datasets used for reporting, dashboards, and model development
Collaborate with business analysts, data engineers, BI teams, and stakeholders to understand business problems and data needs
Create visualizations and reports to communicate insights to technical and non-technical audiences
Support data quality checks, outlier analysis, missing value handling, and model validation activities
Maintain documentation for data sources, assumptions, model logic, analysis results, and business recommendations
<> Required Qualifications
2–3 years of experience in data science, analytics, data analysis, or related roles
Strong SQL skills for querying, joining, aggregating, and validating data
Good Python knowledge using libraries such as Pandas, NumPy, Scikit-learn, Matplotlib, or similar tools
Understanding of statistical analysis, data cleaning, feature engineering, and machine learning basics
Experience with exploratory data analysis, trend analysis, and business insight generation
Basic understanding of banking, financial services, payments, lending, risk, or customer analytics is preferred
Exposure to cloud platforms such as AWS, Azure, or similar environments is a plus
Understanding of data warehousing, data pipelines, and structured datasets
Strong analytical mindset with attention to data accuracy and business context
Good communication skills and ability to explain data insights clearly to business stakeholders
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