Role Overview:

We are seeking a talented and experienced Data Scientist to join our banking team. The successful candidate will apply advanced data analytics, statistical modeling, and machine learning techniques to solve business challenges in a dynamic banking environment. This role offers the opportunity to work on a variety of projects, from risk management and fraud detection to customer segmentation and predictive analytics, all while driving business decisions that impact our customers and bottom line.

Key Responsibilities:

Fraud Detection & Prevention:

Design and implement machine learning models to detect and prevent fraudulent activities and transactions.

Analyze transaction data and identify patterns or anomalies to improve fraud detection systems.

Risk Management:

Develop models to assess credit risk, loan defaults, and other operational risks.

Provide analytical insights and recommendations to support the bank’s risk mitigation strategies.

Customer Segmentation & Personalization:

Apply clustering and classification algorithms to segment customers based on financial behaviors, demographics, and transaction history.

Develop personalized banking experiences by recommending targeted financial products such as loans, credit cards, and investment opportunities.

Predictive Modeling & Forecasting:

Build predictive models to forecast customer behavior, loan defaults, churn rates, and financial trends.

Leverage time series analysis to predict market trends, interest rates, and other key financial metrics.

Data Analysis & Insights:

Conduct exploratory data analysis (EDA) to identify trends, correlations, and insights that drive business decisions.

Use data visualizations to present complex data in clear, actionable formats for stakeholders.

Automation & Optimization:

Automate and optimize data pipelines, model training, and data reporting processes to improve efficiency.

Continuously evaluate and refine models to ensure accuracy and relevance to changing business needs.

Collaboration & Reporting:

Work closely with internal stakeholders, including risk management, marketing, finance, and IT teams, to understand business needs and deliver data-driven solutions.

Present findings, strategies, and actionable insights to senior management and non-technical stakeholders.

Regulatory Compliance:

Ensure that data models and processes comply with financial regulations and standards, including data privacy and security laws (e.g., GDPR, KYC, AML).

Maintain model transparency and document processes to adhere to industry and regulatory requirements.

Key Skills & Qualifications:

Programming:

Proficiency in Python or R for data analysis and machine learning.

Strong SQL skills for querying and manipulating financial data.

Experience with big data technologies such as Hadoop, Spark, or cloud platforms (AWS, Azure, GCP) is a plus.

Statistical Modeling & Machine Learning:

Experience in applying machine learning algorithms such as regression, classification, clustering, and recommendation systems to solve business problems.

Knowledge of time series analysis, predictive analytics, and econometric modeling for forecasting and financial trend analysis.

Financial & Banking Knowledge:

Strong understanding of banking operations, financial products (e.g., loans, mortgages, credit cards), and services.

Familiarity with financial regulations such as Basel III, KYC, and AML, and experience implementing these in data-driven projects.

Understanding of credit scoring, loan risk assessment, and fraud detection strategies.

Data Engineering & Analysis:

Ability to work with large financial datasets and perform data wrangling, transformation, and cleaning using tools like Pandas, NumPy, or similar.

Experience building and maintaining data pipelines for reporting and analysis.

Data Visualization & Reporting:

Proficiency in tools like Tableau, Power BI, or Python libraries (Matplotlib, Seaborn) to present data insights.

Ability to clearly communicate complex findings to non-technical audiences, including business stakeholders and senior management.

Problem-Solving & Critical Thinking:

Strong analytical skills and the ability to tackle complex, unstructured data challenges in a fast-paced financial environment.

Demonstrated ability to work independently and in collaboration with cross-functional teams.