ZI Ziad Ibrahim
Open to Data Science for Marketing & Analytics opportunities

Marketing × Data × Machine Learning

I turn customer and product data into smarter business decisions.

I'm Ziad Ibrahim, a marketing graduate building at the intersection of customer analytics, machine learning, and business strategy. I use data to understand customer behavior, uncover patterns, and translate analysis into practical marketing recommendations.

Explore my work View resume
Ziad Ibrahim

About me

A marketer who learned to work with the data behind the decisions.

My academic background is in marketing, but I became increasingly interested in the data behind customer behavior: why people buy, why they leave, how campaigns perform, and how businesses can make better decisions from those signals.

That curiosity led me to Python, SQL, statistics, machine learning, Excel, Tableau, and Google Analytics 4. Today, I am building projects that connect technical analysis with real marketing questions instead of treating data science and marketing as separate disciplines.

I also have hands-on AI training experience evaluating text, audio, and visual model outputs, which strengthened my attention to detail, consistency, and analytical judgment.

Selected work

Projects built around business questions, not just models.

Each project follows the same principle: define the business problem, understand the data, choose the right analytical method, and communicate what the business can do next.

Customer Segmentation
Customer segmentation dashboard screenshot
Customer segmentation analysis visualization
Customer segmentation dashboard and business insights
01 Customer Analytics · Machine Learning

Customer Segmentation Analysis

Built a customer-level analytical dataset from e-commerce transactions and used K-Means clustering to identify meaningful customer groups based on spending, order frequency, average order value, and recency.

Business question

How can the company treat different customer groups with more relevant marketing strategies?

What I did

SQL aggregation, feature engineering, scaling, K-Means, Elbow Method, interpretation, and dashboards.

Marketing output

Recommendations for VIPs, frequent buyers, lower-value customers, and inactive customers.

Python SQL Pandas NumPy Scikit-learn Excel Tableau
View GitHub repository ↗ Full case study coming next
02
Coming soon

Product Analytics

Product Analysis Dashboard

Excel-based analysis focused on product performance, KPIs, customer behavior, and decision-ready reporting.

ExcelAnalyticsDashboard
03
In progress

Predictive Marketing

Customer Purchase Propensity

Predicting purchase likelihood from customer behavior using statistics, feature engineering, SQL, and machine learning.

PythonSQLMLStatistics
04
Planned

Campaign Analytics

Marketing Campaign Analysis

Analyzing marketing performance, response behavior, key campaign metrics, and opportunities for optimization.

PythonExcelTableau

Experience

Practical experience across data projects and AI training.

I have developed hands-on experience by building end-to-end data science and marketing analytics projects, alongside professional AI training and evaluation work.

Independent Portfolio Projects Ongoing

Data Science & Marketing Analytics Projects

Applied analytics, machine learning, dashboards, and business recommendations

  • Built an end-to-end customer segmentation project using Python, SQL, K-Means, Excel, and Tableau.
  • Engineered customer-level features from transactional data, including spending, frequency, average order value, and recency.
  • Translated analytical results into practical marketing recommendations for retention, loyalty, reactivation, and personalization.
Outlier 3 months

AI Trainer

Text, prompt, and audio evaluation

  • Evaluated AI-generated responses for quality, relevance, and instruction following.
  • Reviewed prompts and model outputs using structured evaluation criteria.
  • Worked on audio-focused tasks including recording and evaluating spoken content.
Remo Expert 6 months

Generalist AI Trainer — Visual Content

Visual generative AI evaluation

  • Evaluated AI-generated visual content against quality and task-specific criteria.
  • Reviewed images for visual inconsistencies, prompt alignment, and instruction-following issues.
  • Provided consistent human feedback to support model improvement.

Toolkit

The tools I use to move from raw data to a marketing recommendation.

01

Programming & Data

Python · NumPy · Pandas · SQL · Jupyter · Data Cleaning · Feature Engineering

02

Machine Learning

Scikit-learn · Regression · Classification · Clustering · Model Evaluation · K-Means

03

Marketing Analytics

Customer Analytics · Segmentation · Campaign Analysis · Marketing Metrics · Google Analytics 4

04

Visualization & BI

Excel · Tableau · Matplotlib · Dashboard Design · Data Storytelling

05

Math & Statistics

Probability · Statistics · Linear Algebra · Calculus · Mathematical Foundations for ML

06

Additional

Algorithms · Data Structures · Git · GitHub · Prompt Engineering · HTML · JavaScript

Education & learning

A business foundation with a technical learning path.

University

Bachelor's Degree — Faculty of Business

Alexandria University

Marketing specialization with a foundation in consumer behavior, market research, business strategy, and marketing principles.

Professional learning

Data, ML & Marketing Analytics

  • Machine Learning Specialization
  • Meta Marketing Analytics
  • IBM AI Developer
  • Mathematics for Machine Learning
  • Probability & Statistics
  • Google Analytics 4

Let's connect

Looking for someone who understands both customers and data?

I'm interested in opportunities across Data Science for Marketing, Marketing Analytics, Customer Analytics, AI training, and applied machine learning.

Email me LinkedIn ↗ GitHub ↗