Adnan Ferrie Fotsing

With over five years immersed in data analytics, I have honed a diverse skill set that drives business growth through insightful analyses. My expertise encompasses tools like SQL, Python, R, Power BI, and Tableau, ensuring data is not just interpreted but also visually compelling. My recent tenure as a Senior Data Analyst at American Express exemplified my capability to manage complex projects efficiently, extract meaningful insights, and support strategic decisions. As a graduate from Brunel University with a specialization in Data Sciences and Analytics, my academic and professional journeys have synergized to make me adept at both machine learning applications and high-performance computing. Fluent in both French and English, I bring a global perspective to every project, ensuring collaboration and communication are always at the forefront.

Senior Data Analyst                                                                                                                                               American Express (Contract)                                                                                                                                March 2023 – June 2023

• Managed SQL ad-hoc requests from managers, translating business requirements into efficient SQL queries.
•     Extracted and transformed data from a data lake, exporting it to Excel files for comprehensive analysis.
•     Conducted in-depth analysis using pivot tables in Excel to derive valuable insights and support decision-making.
•     Developed advanced SQL queries to handle complex data scenarios, ensuring optimized data retrieval.
•     Implemented rigorous data validation, cleansing, and quality assurance procedures to maintain data integrity.
•     Collaborated with other departments by helping them leverage the power of data for their marketing campaign.
•     Develop and maintain the architecture governance analytical views using bank approved data analytics tools to promote data-driven architecture governance

Data Analyst                                                                                                                                                          Lepaval Data Analytics - Brantford, ON                                                                                                         January 2022 to Present

            Credit Risk Data Analyst, RBC (Contract)

• Utilized SQL, Python, SAS, Excel for comprehensive data management including mining, cleansing, interpretation, and transformation, leading to sophisticated data reporting.
• Leverage advanced analytics techniques to identify trends and pattern for optimization within the collections process using Power Bi and Excel.
• Developed and implemented collection strategies and procedures to maximize the recovery and redemption rate, and minimize losses on credit card and auto loans
• Engineered and executed tailored financial data queries to address specific business requirements.
• Designed and implemented visually engaging Power BI dashboards, to provide real-time overview into collection performance metrics such as recovery rates.
• Develop and maintain the architecture governance analytical views using bank approved data analytics tools to promote data-driven architecture governance

Key accomplishments:

• Automated data extraction and reporting processes: Streamlined data extraction and reporting processes using Power Bi and SAS, resulting in significant time saving.
• Implemented automated workflow to reduce manual error and ensure timely delivery of accurate reports to stakeholders

Data and Analytics Specialist, Bank of Canada

Financial Markets Monitoring (Tableau)

• Utilized Databricks to create views in the unity catalogue, enabling centralized storage and access to financial data.
• Developed interactive Tableau dashboards for monitoring Canadian government bonds, currency exchange rates, and interest rates.
• Imported views from Databricks into Tableau to access the necessary variables for visual analysis.
• Collaborated closely with economists to create tailored dashboards serving as starting points for their in-depth financial analysis.
• Tracked the historical performance of Canadian government bonds, including present, past, and forecasted data.
• Monitored the outstanding amount of bonds in circulation and provided insights for decision-making.
• Implemented data visualization techniques to enhance the understanding of financial trends and market dynamics.
• Employed dynamic filters and slicers in Tableau for interactive data exploration, allowing stakeholders to drill down into specific data points, such as different maturity buckets, currencies, and benchmark types.

Key Achievements:

• Created dynamic Tableau dashboards that allowed economists to analyse the performance of Canadian government bonds over time, resulting in more informed investment decisions.
• Implemented data visualization techniques, including calculated fields, to compare predicted vs. actual CAD/USD exchange rates over time, enabling
• Developed a comprehensive financial market dashboard that integrated real-time data on currency exchange rates, interest rates, and bond performance, improving the speed and accuracy of analysis.
• Collaborated with data engineering team to optimize data extraction and integration

Senior Data Analyst                                                                                                                                     CATRASCO Sarl • Douala                                                                                                                                        Jan 17 – Aug 21

• Performed risk management by conducting exhaustive risk analysis, predicting potential challenges in engineering projects, and devising strategies to mitigate these risks, ensuring project stability and success.
• Utilized project management software and tools to monitor project progress, identify potential risks, and adjust project schedules and budgets accordingly.
• Conducted regular data analysis to identify trends and opportunities for process improvement, resulting in a 15% increase in project efficiency and cost savings.
• Utilizing Power BI, I developed visually compelling dashboards which served as an essential tool for real-time tracking of project metrics and progress, thus enhancing transparency and communication among stakeholders.
• Routinely presented data findings to stakeholders, translating complex data into understandable insights, and enabling informed decision-making and strategic planning, pivotal to the continued growth and success of the organization.

Master of Sciences in Data Sciences and Analytics                                                                             Sep21 – Sep 22                                                                                                                                            Brunel University London • London, United Kingdom

Key Modules: Quantitative Data Analysis, High Performance Computational Infrastructure, Machine Learning, Distributed Data Analysis and Data Visualization Dissertation – Risk Prediction of Type 2 Diabetes and Risk Factors Analysis: A Machine learning Classification Approach (Python, R and Excel)                                                                                                   

• Built five classification machine learning methods for predicting patients at risk of Type 2 Diabetes (T2D), namely Decision Tree, Random Forest, K-Nearest Neighbor, Logistic Regression and Support Vector Machine 

Distributed Data Analysis – Modelling Used cars Resell Prices with Machine Learning           

• Merged datasets of different car brands in the United Kingdom into a single large used car data
• Cleaned data and use unsupervised machine learning techniques (PCA) to conduct data exploration to reduce dimensionality by extracting the most valuable features.
• Developed a Neural Network Model (R) and Linear Regression Model (Pyspark) to predict the average resell price of used cars across different car manufacturers.
• Critically evaluating models’ performance using Mean Square Error, Root mean Square Error and Mean Absolute Error.