muhiacollins
About Candidate
Location
Education
A quantitative degree focused on the application of mathematics, probability, statistics, and financial theory to identify, model, and manage risk. The program covers actuarial modeling, financial economics, insurance and pension mathematics, risk management, and data analysis, preparing graduates for roles in insurance, banking, investment, and financial analytics.
Certified in Advanced Microsoft Excel, Word, PowerPoint, and Computer Essentials, with practical experience in data analysis, spreadsheet modeling, document preparation, professional reporting, and effective presentation design. Demonstrates strong computer literacy, accuracy, and efficiency in handling digital tools for academic and professional tasks.
Building practical expertise in data analytics, machine learning, and statistical modeling with a strong focus on financial and economic data. Applying Python-based data analysis, exploratory data analysis (EDA), and predictive modeling techniques to real-world datasets. Gaining experience in working with large datasets, feature engineering, and model evaluation for data-driven decision-making in finance and business contexts.
Work & Experience
Designed, maintained, and enhanced financial and KPI tracking models used to monitor product performance, portfolio health, and operational efficiency across business units. Performed cash flow trend analysis and short- to medium-term forecasting to support business planning, budgeting, and product strategy decisions. Extracted, cleaned, and analyzed large datasets using SQL and advanced Excel techniques to produce accurate, timely management reports and performance summaries. Collaborated closely with product managers and business analysts to translate business requirements into data-driven insights, supporting pricing decisions and profitability analysis. Developed and automated dashboards and reporting frameworks, reducing manual reporting effort and improving the speed and quality of decision-making across teams.
Collected, verified, and processed industrial production data from multiple firms and sectors in line with national statistical standards and reporting frameworks. Conducted data validation and cleaning using R and Microsoft Excel to improve data accuracy, consistency, and reliability prior to analysis and publication. Analyzed production trends and key indicators to support sectoral analysis and evidence-based policy reporting. Worked closely with senior statisticians and supervisors to interpret complex datasets and convert analytical results into clear summaries for internal reports and national statistics. Adhered to strict data confidentiality, quality assurance, and documentation protocols throughout the data collection and reporting process.
