Resume

🎓 Institut Agro Rennes-Angers
📍 Rennes, France

Focus & Impact

• Developed a strong foundation in statistical modeling, machine learning, and applied mathematics
• Applied advanced data science techniques to real-world datasets across multiple academic projects

Key Topics

• Machine Learning & Deep Learning
• Statistical Inference & Optimization
• Data Analysis & Modeling
• Scientific Computing

🎓 Institut National de Statistique et d’Économie Appliquée (INSEA)
📍 Rabat, Morocco

Focus & Impact

• Built a solid engineering background combining data engineering, software development, and applied statistics
• Worked on end-to-end data projects from data collection to deployment

Key Topics

• Data Engineering & Databases
• Software Engineering & System Design
• Applied Statistics & Econometrics
• Algorithms & Data Structures

🎓 Morocco

Focus & Impact

• Completed an intensive two-year preparatory program emphasizing analytical rigor and problem-solving
• Developed strong foundations in mathematics, physics, and algorithmic thinking

Key Topics

• Advanced Mathematics (Analysis, Algebra, Probability)
• Classical & Applied Physics
• Scientific Problem Solving under time constraints

📍 Complex Horticole Agadir CJ, Morocco

Impact

• Designed and delivered industry-oriented courses in Machine Learning, Python, and Algorithmics for second-year students
• Improved students’ practical readiness for data-related roles through project-based learning and hands-on workshops

Responsibilities

• Develop comprehensive curricula aligned with real-world data science and programming practices
• Mentor and supervise student data science projects from problem definition to implementation
• Design practical exercises bridging academic theory and industry use cases

📍 France

Impact

• Reduced operational workload by 30% through AI-driven workflow automation
• Accelerated decision-making by 40% using real-time analytical dashboards

Responsibilities

• Automated repetitive business processes using AI and data-driven solutions
• Designed and deployed Power BI dashboards connected to GIT/DevOps pipelines
• Applied statistical modeling and process mining to optimize resource allocation

📍 Valence, France

Impact

• Contributed to predictive maintenance initiatives by reducing unexpected machine failures
• Enabled operational insights through ML-powered dashboards

Responsibilities

• Built and evaluated predictive maintenance models using SVM, LSTM, and XGBoost
• Tested and validated multiple ML/DL pipelines for industrial use cases
• Integrated model outputs into Power BI dashboards and synchronized code via GIT/DevOps

📍 Casablanca, Morocco

Impact

• Streamlined data exchange between banking and customs systems via secure API integrations
• Improved monitoring and traceability of system interactions through analytics dashboards

Responsibilities

• Developed an ESB intermediary connecting customs platforms with banking systems
• Published SOAP, REST, and HOST web services using Software AG tools
• Designed Power BI dashboards for tracking, managing, and analyzing system exchanges
• Contributed to the bank’s API management initiative

📍 Morocco

Impact

• Supported data-driven decision-making for the USAID program by identifying eligible cooperatives for financial assistance

Responsibilities

• Conducted large-scale data analysis on cooperative datasets using Python
• Applied statistical filtering and eligibility criteria using IDinsight methodologies
• Built Power BI dashboards to visualize and monitor program outcomes

Core Strengths

• Data cleaning, transformation, and exploratory data analysis (EDA)
• Translating business questions into data-driven insights
• Building actionable dashboards and reports for decision-making

Tools & Technologies

• Languages: Python, SQL, R
• Libraries: Pandas, NumPy, Seaborn
• BI & Visualization: Power BI
• Databases: SQL, NoSQL

Core Strengths

• End-to-end machine learning pipelines: data preparation, modeling, evaluation
• Supervised and unsupervised learning for predictive and descriptive analytics
• Model validation, performance evaluation, and interpretability

Tools & Technologies

• Machine Learning: scikit-learn, XGBoost
• Deep Learning: TensorFlow, Keras
• Techniques: Regression, Classification, Clustering, Time Series, Predictive Maintenance

Core Strengths

• Statistical inference and hypothesis testing
• Probabilistic modeling and time series analysis
• Quantitative reasoning for data-driven decision-making

Tools & Techniques

• Descriptive & inferential statistics
• AR, MA, ARIMA models
• Optimization and applied mathematics concepts

Core Strengths

• Building reliable data pipelines and reusable codebases
• Version control and collaborative development
• Deploying data-driven applications and APIs

Tools & Technologies

• Programming: Python, Java, Scala, PySpark, JavaScript
• Frameworks: Flask, Django
• Big Data: PySpark
• Version Control & CI/CD: GIT, Azure DevOps

Core Strengths

• Managing data projects in cloud-based environments
• Automating workflows and synchronizing analytics with DevOps pipelines
• Supporting production-ready data solutions

Tools & Platforms

• Azure Data Analytics & Machine Learning
• Azure DevOps
• CI/CD concepts for data projects

Core Strengths

• Working in cross-functional and agile teams
• Translating technical results into clear business insights
• Managing data projects from requirements to delivery

Methodologies & Tools

• Agile / SCRUM
• UML, BPMN, Merise
• PowerApps (basic integration)

Impact

• Built a computer vision system capable of detecting traffic light panels and identifying displayed colors
• Explored both classical ML and deep learning approaches to improve detection accuracy

Details

• Designed image processing and classification pipelines
• Trained and evaluated models using CNN, SVM, and Random Forest
• Performed comparative analysis of model performance

Tools

• Python, OpenCV
• Deep Learning & Machine Learning algorithms

Impact

• Developed forecasting models to analyze and predict Tesla stock price trends
• Compared classical statistical models with modern data science approaches

Details

• Modeled financial time series using AR, MA, and ARIMA
• Implemented models in both Python and R
• Evaluated model stability and predictive performance

Tools

• Python, R
• Time Series Analysis & Statistical Modeling

Impact

• Estimated cell type proportions in bulk RNA-seq samples using advanced statistical techniques
• Contributed to understanding heterogeneous biological samples

Details

• Implemented and compared multiple deconvolution algorithms
• Applied OLS, RLR, and DeconRNA-Seq methods
• Analyzed model robustness and interpretability

Tools

• R
• Statistical Modeling & Data Analysis