Data Analyst

Role/ Key Responsibilities

Data Analysis Using SAS, SQL, and Python:
• Conduct in-depth analysis of large SME data to identify patterns and trends in customer behavior.
• Utilize SAS, SQL, and Python for comprehensive data analysis.

Process Improvement and Automation:
• Design and automate process improvement tasks, enhancing efficiency and accuracy.
• Source data from Database Server into analytical DataMart using SAS, SQL, and Python.

Statistical and Machine Learning Models:
• Apply statistical and machine learning models to address complex business problems.
• Drive innovation and process optimization through advanced analytics.

Data Cleaning and Pre-processing:
• Perform meticulous data cleaning and pre-processing using SAS and SQL.
• Ensure data accuracy and integrity.

Risk Management and Commercial Opportunities:
• Utilize statistical and machine learning models to identify new commercial possibilities.
• Mitigate risks and contribute to business expansion.

Exploratory Data Analysis (EDA):
• Conduct exploratory data analysis using Python and SQL.
• Provide valuable information for business strategy development.

Database Management:
• Design and manage large databases using SQL, Azure Data Studio, and Databricks.
• Ensure seamless data storage and retrieval.

Dashboards and Visualizations:
• Create interactive dashboards and visualizations using Tableau/Power BI.
• Present findings and insights to stakeholders for effective communication.

Portfolio Monitoring and Reporting:
• Prepare portfolio monitoring reports based on credit performance.
• Segment customers based on risk association and track their performance.

Collaboration and Insights:
• Collaborate with business units to understand analytical needs.
• Develop ad-hoc reporting and analysis, providing valuable insights.

Performance Evaluation:
• Monitor and evaluate the performance of the credit card portfolio.
• Make necessary adjustments to improve performance and mitigate risks.

Collection Strategies:
• Develop strategies for delinquent customers.
• Analyze historical data to determine the effectiveness of previous collection efforts.

Advanced Analytics Techniques:
• Utilize advanced data analytics techniques and business intelligence tools.
• Drive business performance, achieve targets, and inform decision-making processes.

Process Improvement and Automation (Continued):
• Develop and automate process improvement tasks, leading to enhanced operational performance.
• Reduce manual intervention in analytical processes.

Key Requirements

Skills : Data Analysis, Advanced Excel, Data Reporting, Python

Data Analytic, Reporting