Oreoluwa Abiodun

• Developed data analysis plans for complex research projects. 
• Collected large sets of data from various sources, such as financial databases, market data, customer information, and external sources like social media.
• Accounted for cleaning, transforming, and preparing data to ensure that it was accurate and complete. And also combined data sets from different sources and format the data for analysis. 
• Implemented the use of statistical and mathematical methods to analyze data and find insights. Created reports and dashboards to visualize the data and communicate findings to the stakeholders.
• Worked on risk management-related projects that includes developing credit risk models, detecting fraud, identifying compliance issues, and more.
• Provided analytical support to the business strategy team identifying trends, and developing insights that were used to make informed decisions.
• Automated data collection and analysis processes to save time and improve efficiency. Also identified opportunities for process improvement and developed solutions to improve workflows.
• Collaborated with other analysts, data scientists, and business stakeholders to understand business requirements, shared insights, and work on projects.
• Worked on credit risk assessment projects with the analysis of creditworthiness using statistical models to assess risk and develop strategies to mitigate it.
• Contributed to marketing efforts with the use of data analysis, such as customer segmentation, targeting, and campaign optimization, and identified opportunities for upselling by analyzing customer behavior and preferences.
• Worked on projects related to monitoring anti-money laundering (AML) activities, fraud detection, and ensuring adherence to privacy regulations like GDPR or CCPA.
• Analyzed investment portfolio performances and made recommendations for asset allocation and diversification. In addition, identified potential investment opportunities and evaluated risk levels
• Worked on projects related to business intelligence, such as developing KPIs, measuring business performance, and providing insights to senior management. 
• Utilized data visualization tools to create reports and dashboards to communicate insights to stakeholders and also used machine learning algorithms to detect anomalous behavior and flag suspicious transactions.

Certified Data Analyst