Mathematical Scientist & Financial Engineering Candidate — specialising in the synthesis of high-level quantitative theory and actionable business intelligence. I transform multi-dimensional, noisy datasets into rigorous, evidence-based decision frameworks.

My foundation is an MSc in Mathematical Sciences (AIMS Ghana), rooted in system dynamics and signal processing — specifically, how feedback loops respond to oscillatory inputs. I am currently extending this into an MSc in Financial Engineering (WorldQuant University), focusing on derivative pricing, risk management, and the optimisation of financial systems.

I diagnose operational inefficiencies through stochastic modelling and interactive dashboarding, and translate findings into measurable institutional impact.

📬 Find me online:

• Microsoft Certified: Power BI Data Analyst Associate (PL-300) ID: 12F8C48C8B1E470A

• Foundations of Financial Engineering – WorldQuant University (May 2026)
• Advanced Topics in Derivative Pricing – Columbia University
• Optimization Methods in Asset Management – Columbia University
• Introduction to Financial Engineering and Risk Management – Columbia University

• Modeling Risk and Realities – University of Pennsylvania
• Decision-Making and Scenarios – University of Pennsylvania
• Fundamentals of Quantitative Modeling – University of Pennsylvania
• Linear Regression for Business Statistics – Rice University
• Business Applications of Hypothesis Testing – Rice University
• Statistical Distributions & Business Decisions – Rice University

• Business Management – ESMT Berlin & iiAfrica (IIP Programme)
• Excel Skills for Business Specialization – Macquarie University
• Mindware: Critical Thinking for the Information Age – University of Michigan
• Introduction to Spreadsheets and Models – University of Pennsylvania

Completed at AIMS Ghana (2025) My research focused on the mathematical mechanics of biological regulatory pathways and system stability:

• Oscillatory Dynamics: Analyzed how negative feedback loops respond to periodic signals—a concept directly applicable to modeling financial market cycles and asset volatility.
• Mathematical Modeling: Applied differential equations to evaluate system stability and signal processing within biochemical networks.
• System Analysis: Studied how timing and degradation rates determine the success of oscillations in repressive feedback networks.
• My research in mathematical sciences focused on the stability of dynamic systems under oscillatory pressure. I apply these same differential equations to financial markets to model how asset prices respond to cyclical economic signals and to identify the feedback thresholds that lead to market volatility.

Currently pursuing an MSc at WorldQuant University, with a technical focus on:

• Derivative Pricing & Risk: Modeling financial instruments using stochastic calculus and PDEs.
• Portfolio Optimization: Applying linear algebra and quadratic programming to asset management.
• Econometrics: Using time-series analysis to forecast market volatility and economic shocks.

Evaluated a decade of macroeconomic data (2015–2025) to model fiscal recovery and institutional transition:

• Modeled the structural synergy between copper price volatility and national debt trends.
• Identified how the 2020–22 copper windfall provided the fiscal headroom for durable institutional frameworks and debt restructuring.
• Developed interactive Power BI dashboards to visualize complex interactions between commodity cycles and investor confidence.

Built high-performance predictive models to inform data-driven customer retention strategies:

• Implemented XGBoost and Logistic Regression with engineered features and RobustScaler preprocessing.
• Applied SMOTE to address class imbalance, improving minority-class recall by 22% and overall accuracy by 12%.
• Achieved an ROC-AUC of 0.81 (XGBoost) and 0.74 (Logistic Regression) to identify key churn drivers.

Consolidated disparate monthly sales datasets into a unified data model for NorthWind Outfitters:

• Used Power Query and XLOOKUP to track revenue trends, profit margins, and regional performance.
• Optimized inventory control by monitoring stock levels, reorder points, and supplier lead times.
• Built evidence-based visual frameworks to diagnose operational inefficiencies.

Jul 2022 – Feb 2024

• Instructional Leadership: Delivered comprehensive mathematics and statistics tutorials, enabling medical and health sciences students to master quantitative problem-solving.
• Academic Mentorship: Provided dedicated mentorship to students, guiding them through the application of statistical methods in clinical data and research contexts.
• Coursework Management: Supported grading and maintained rigorous academic records for assigned cohorts, ensuring high standards of educational excellence.

Business Intelligence Solutions: Successfully completed an intensive technical bootcamp focused on architecting and implementing BI solutions for high-stakes client engagements.

• ETL & Data Integrity: Leveraged Power Query to extract and clean complex transactional datasets, ensuring absolute data integrity for organizational reporting.
• Dashboard Engineering: Developed robust Power BI dashboards that provide stakeholders with visual frameworks for rigorous, evidence-based decision-making.
• Performance Optimization: Engineered advanced DAX measures and optimized data models, significantly increasing reporting speed and analytical accuracy.
• Data Profiling: Conducted systematic data profiling to identify structural inconsistencies, directly elevating the quality of organizational data assets.