Financial Analyst Data Analytics

Mohamed Kenneh

Financial Analyst Data-Driven Decision Support SQL • Python • Tableau

***************@*****.*** 281-***-**** Houston, TX Open to Relocation LinkedIn SUMMARY

Results-driven Financial Analyst with a B.S. in Energy Business & Finance and hands-on experience in financial modeling, forecasting, variance analysis, and data visualization. Skilled in tools such as SQL, Python, Excel, and Tableau, with a proven ability to automate reporting workflows, enhance procurement efficiency, and deliver actionable insights that drive strategic decisions. Adept at transforming complex financial and operational data into executive-level recommendations. Former professional athlete with a track record of discipline, adaptability, and high-performance execution in global environments — now applying the same competitive mindset to analytical problem-solving and cross-functional business impact.

Education

University of Maryland Global Campus – Master of Science in Financial Management Aug 2022-Dec 2025 Pennsylvania State University Aug 2016 – Dec 2020

Earth and Mineral Science

Bachelor of Science in (Energy Business and Finance) Minor in Environmental Engineering and Social Entrepreneurship Coding Temple- Data Analytics Apprenticeship Oct 2024 -Mar 2025 SKILLS

Financial Analysis & Modeling: Financial forecasting, Cost modeling, Variance analysis, Budgeting, Sensitivity analysis, KPI tracking. Technologies & Tools: Excel (PivotTables, Macros, VLOOKUP, Financial Functions), SQL, Power BI, Tableau, Python (Pandas, NumPy), Jupyter Notebook

Reporting & Visualization: Dashboard creation, Monthly reporting packages, Ad hoc analysis, Executive summary reporting, Data storytelling

Business & Soft Skills: Cross-functional collaboration, Strategic thinking, Stakeholder communication, Problem-solving, Deadline- driven execution

EXPERIENCE

Coding Temple – Data Analytics Consultant Repositories Oct 2024- Jun 2025

• Built a predictive financial model to forecast housing prices using Python and Scikit-learn, achieving 86%+ R accuracy across 10+ engineered features.

• Designed dashboards in Tableau and Streamlit to highlight financial trends, cost drivers, and performance metrics.

• Used SQL and Pandas to clean, transform, and analyze raw datasets, reducing data inconsistencies and improving reporting quality.

• Applied regression model optimization techniques to improve forecast precision and business relevance.

• Analyzed over 1 million Instacart orders to identify trends in delivery performance, customer retention, and reported issues.

• Demonstrated how targeted, data-driven changes could reduce support wait times and improve Net Promoter Score (NPS).

• Presented insights and ROI-based recommendations to a simulated executive board using PowerPoint, emphasizing key KPIs and operational impact.

Rio Tinto – Financial Analyst (Consultant – Procurement Insights) Feb 2021 – May 2022

• Analyzed vendor and procurement data to support cost modeling, contract pricing, and sourcing decisions across global operations.

• Automated monthly reporting in Excel using macros, pivot tables, and VLOOKUP, reducing manual input by over 30%.

• Built dashboards to monitor KPIs, cost trends, and vendor performance, supporting billing cycle accuracy and revenue optimization.

• Used SQL and Python to clean and structure raw financial data, enabling faster decision cycles for procurement teams.

• Presented actionable insights to cross-functional stakeholders, supporting risk mitigation and sustainability alignment. Penn State University Sustainability Research Assistance-USDA Environmental Field Study (Python, Tableau, SQL, Excel)

● Collected and analyzed performance data on irrigation systems, energy use, and water efficiency to support operational benchmarking and utility cost assessment.

● Leveraged Excel to clean, aggregate, and visualize field data, identifying trends and variances in resource utilization across multiple sites.

● Supported the development of KPI frameworks to evaluate sustainability impact, cost-efficiency, and system-level performance improvements.

● Ensured data integrity and compliance with environmental protocols, contributing to risk mitigation and operational consistency.

● Presented actionable findings to research teams to drive process improvements and long-term resource planning.

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