•Spear a predictive analytics project to forecast future sales trends, which contributed to more informed decision-making and a 5% reduction in unnecessary expenditure. Utilized predictive analytics to anticipate customer churn rates, providing the company with insights that led to a reduction in customer attrition by 10%.• Assisted in the implementation of a new customer relationship management(CRM)system, ensuring the efficient migration of all relevant data, and training the marketing… Show more •Spear a predictive analytics project to forecast future sales trends, which contributed to more informed decision-making and a 5% reduction in unnecessary expenditure. Utilized predictive analytics to anticipate customer churn rates, providing the company with insights that led to a reduction in customer attrition by 10%.• Assisted in the implementation of a new customer relationship management(CRM)system, ensuring the efficient migration of all relevant data, and training the marketing team on using the system to access the most useful data.•Collaborated with the sales and marketing teams to build customer profiles for new product feedback and personas based on analysis findings for resume services, which were instrumental in designing more targeted marketing campaigns.•Collaborated with sales and marketing teams, utilizing Tableau to create detailed customer profiles and personas, incorporating soft skills like effective communication and teamwork, enhancing targeted marketing campaigns and overall effectiveness. Show less

• Utilized Excel VBA and SQL, along with Python libraries like panda sandspit-learn, to conduct thorough analysis of large datasets and develop financial models, calculating metrics like Weighted Average Cost of Capital (WACC) and Internal Rate of Return (IRR), resulting in enhanced risk management processes and a 15% reduction in bad debt risk.• Applied statistical analysis techniques in Python, including regression models and clustering algorithms, to uncover valuable trends related to… Show more • Utilized Excel VBA and SQL, along with Python libraries like panda sandspit-learn, to conduct thorough analysis of large datasets and develop financial models, calculating metrics like Weighted Average Cost of Capital (WACC) and Internal Rate of Return (IRR), resulting in enhanced risk management processes and a 15% reduction in bad debt risk.• Applied statistical analysis techniques in Python, including regression models and clustering algorithms, to uncover valuable trends related to risk factors such as market volatility, credit default probabilities, and customer behaviour anomalies, contributing to a 12% reduction in credit risk exposure.• Conducted due diligence analysis on credit products, delivering comprehensive credit analysis reports to clients and focusing on metrics such as liquidity ratio, delinquent accounts percentage, and debt-to-income ratio, achieving a 10% improvement in decision-making accuracy, a 20% decrease in default rates and a 15% increase in financial product sales.• Created a data base with ER diagrams for in-depth analysis of Complex Investigations, FraudRisk, and Incident Management Resolution (IMR), resulting in a 25% reduction in fraud-related losses. Show less