Mohamed Gebril
Lubbock, United States |-| - | Mohgeb.rf.gd | LinkedIn | GitHub
Education
August 2022 – Present
Texas Tech University, BS in Mathematics, Applied Mathematics (GPA: 3.1/4.0)
• Coursework: Number Theory, Algebraic Topology, Combinatorics Theory, Mathematical Computation, Advanced
Calculus, Differential Equations
Texas Tech University, BS in Biology, Genetics (GPA: 3.1/4.0)
August 2022 – Present
• Coursework: Genetics, Microbiology, Organic Chemistry, Zoology, Cell Biology, Organic Evolution
Experience
June 2024 – Present
Junior Data Analyst, Higgs Web Development – Alexandria, Egypt (Remote)
• Conducted 25 competitive analyses quarterly to track market trends, identify pricing strategies, and predict
15% quarterly market growth for targeted niches, enabling the startup to optimize its pricing model
effectively. quality by 25% over three quarters.
• Applied demographic analysis using a Python tool to enhance market research, identifying 5+ key audience
segments for targeted service strategies.
Applied Mathematician, Outlier AI – Liverpool, United Kingdom (Remote)
Jan 2024 – Present
• Enhanced AI model training efficiency by 20% by developing intricate mathematical models focused on key
metrics such as truthfulness and credibility, and translating them into optimized Python code.
• Improved LLM satisfaction scores by 12%, using parameter tuning and iterative modeling techniques to address
user feedback in real-world applications.
Projects
Medical-Data-Visualizer
Medical-Data-Visualizer
• Developed a Python tool to visualize over 1,000 patient records, utilizing Seaborn and Matplotlib to
generate categorical plots and heat maps, aiding users in identifying trends in health indicators like cholesterol,
glucose, and BMI.
• Improved data-driven insights for healthcare professionals by illustrating key correlations between 12 medical
variables, facilitating a 20% faster identification of high-risk patients.
Demographic-Data Analyzer
Demographic-Data Analyzer
• Analyzed demographic data from 32,500+ entries in the 1994 Census database using Pandas, uncovering
distributions in race, education levels, income brackets, and working hours.
• Provided actionable insights into workforce diversity, showing a 20% disparity in education levels across
income brackets, aiding policymakers in understanding socioeconomic trends.
Sea-Level-Predictor
Sea-Level-Predictor
• Predicted future sea level rise through 2050 by analyzing 139 years of historical data -) using
machine learning techniques and regression models, generating visualizations with Matplotlib and Pandas to
communicate findings effectively.
• Enhanced environmental impact studies by achieving a 95% correlation accuracy between historical data
trends and predicted sea-level changes.
Technologies
Languages: Python, JavaScript, VBA, HTML, CSS, R, SQL
Technologies: Excel, Power BI, Pandas, Seaborn, Matplitlib, SciPy, Biopython, NumPy, TensorFlow,
Awards and Certificates
Certificates: Data Analysis with Python, Responsive Web Design
