SAS Data Analyst

New Delhi, India

Full Time Entry-level / Junior USD 29K - 55K * ^est.

Inspira

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Posted 3 weeks ago

Data Analysis & Reporting:
- Analysis of complex financial datasets (e.g., customer, transaction, loan portfolios) and create reports/dashboards using SAS Visual Analytics.
- Design and implement data models for risk analysis, fraud detection, credit scoring, and forecasting, financial performance in the BFSI domain.
- Conduct predictive analytics and forecasting to guide decisions on customer retention, loan defaults, and portfolio performance.
- Ensure compliance with regulatory frameworks and analyze risks across financial data.
- Collaborate with cross-functional BFSI teams to define data requirements, communicate insights, and optimize reporting processes.
- Maintain data quality, governance, and stay updated with industry trends to improve analytics capabilities.
- Lead and manage the end-to-end data analysis process, from data extraction and transformation to visualization and reporting.
- Utilize SAS Visual Analytics to perform advanced analytics and build interactive, actionable reports and dashboards.
- Develop and maintain visual dashboards to provide actionable insights to business leaders.
- Apply advanced analytical techniques for predictive modeling, including forecasting.
Data Modeling & Visualization:
- Design and implement data models to support complex reporting and data visualization solutions using tools like SAS VA, Power BI, Tableau, and Qlik.
- Collaborate with business stakeholders to define key metrics and design meaningful data models that align with business objectives.
- Ensure best practices are followed in data modeling, visual design, and report/dashboard development.
Forecasting & Predictive Analytics:
- Conduct predictive analytics and forecasting using advanced techniques in DAX and SAS to support business decision-making.
- Work with historical data to generate forecasts, trends, and predictive models that aid in strategic planning and operational decisions.
Collaboration & Stakeholder Management:
- Work closely with business users, IT teams, and data engineers to identify data requirements and ensure the accurate delivery of data insights.
- Provide mentorship and guidance to junior data analysts, fostering a collaborative and knowledge-sharing environment.
- Communicate findings and insights to non-technical stakeholders, explaining complex data concepts in a simple, actionable manner.
Data Governance & Quality:
- Ensure the quality, consistency, and accuracy of data by applying data governance best practices.
- Proactively identify data quality issues and work with relevant teams to resolve them.
Continuous Improvement:
- Stay current with industry trends and best practices in data analytics, visualization, and forecasting.
- Propose and implement process improvements to enhance the efficiency and effectiveness of analytics and reporting.