John Jerry Gordon-Mensah

Led a comprehensive financial analysis of BigTechCompany to evaluate its historical performance, customer trends, and future financial outlook. Key responsibilities and outcomes included:

Financial Analysis: Conducted in-depth Excel analysis of quarterly and annual revenue, operating income, net income, and free cash flow.
Data Projections: Developed financial projections for 2023 and 2024, incorporating growth rates and key metrics such as CAGR and EBITDA margin.
Visualization: Created a professional PowerPoint presentation summarizing financial insights and recommendations.
Problem Solving: Identified and addressed areas of concern, such as EBITDA margin gaps and unusual expense trends, while crafting actionable solutions.
Communication: Delivered insights through a video presentation, showcasing strong verbal and visual storytelling skills.
Skills Gained:
Communication: Clearly articulated financial insights through presentations and video recordings.
Critical Thinking: Identified trends and anomalies in financial data, drawing actionable conclusions.
Excel: Utilized advanced Excel techniques for data analysis and projections.
Financial Analysis: Evaluated profitability, growth trends, and key metrics to provide strategic recommendations.
Problem Solving: Addressed complex financial challenges, such as attrition and margin improvement opportunities.

During the BCG X job simulation, I took on the role of a Junior Data Scientist working with PowerCo, a major energy utility for SMEs. My main objective was to develop a predictive model to identify customers at risk of churning. I conducted exploratory data analysis, feature engineering, and built a Random Forest classifier that achieved an accuracy of 82% in predicting customer churn.

The insights I provided highlighted key factors driving churn, such as net margin and price fluctuations, rather than customer price sensitivity. I proposed a targeted discount strategy to retain high-risk customers, which could potentially save the company up to $10 million annually. This experience strengthened my skills in data analysis, machine learning, and effectively communicating insights to stakeholders.

• Conducted a comprehensive data analysis of the chips category, focusing on consumer behavior and trial store performance.
• Leveraged Python and Jupyter Notebook for data wrangling, validation, visualization, and statistical testing.
• Developed customer segmentation models to identify growth opportunities among young and older families.
• Created actionable recommendations, including expanding trial layouts and optimizing SKU placements for small and medium pack sizes.
• Presented findings in a professional PowerPoint report, utilizing commercial thinking and storytelling with data.

• Applied advanced data analytics techniques—such as word clouds—to extract and analyze sentiment from customer reviews.
• Performed comprehensive exploratory data analysis to identify patterns and trends, and used clear visualizations and reports to guide project direction.
• Demonstrated expertise in data mining and sentiment analysis to deliver actionable business insights.
• Developed predictive models to identify key factors driving purchasing behavior.
• Improved machine learning algorithms by integrating data preprocessing and feature engineering techniques.
• Utilized time series forecasting techniques to anticipate future sales trends, facilitating informed decisions on resource allocation.
• Optimized machine learning models for improved prediction accuracy and performance.
• Developed custom algorithms to solve unique challenges in data analysis and modeling.

• Built an 87%-accurate predictive model for customer bookings, enhancing targeting and marketing efficiency.
• Analyzed content trends across seven datasets, uncovering insights that informed strategic decisions for a hypothetical social media client.
• Created engaging visuals and dashboards to communicate key findings, developing a PowerPoint deck and video presentation for internal and client stakeholders.
• Completed a data simulation focused on data cleaning, modeling, and storytelling, further refining skills in data analysis and strategic insight delivery.
• Supported staff members in their daily tasks, reducing workload burden and allowing for increased focus on higher-priority assignments.
• Gained valuable experience working within a specific industry, applying learned concepts directly into relevant work situations.