I am an Applied Data Scientist and ML Engineer with end-to-end experience building machine learning products, from problem definition and experimentation through production deployment, monitoring, and measurement.
I specialize in decision intelligence, machine learning pipelines, retrieval-augmented generation (RAG), LLM applications, and agentic AI workflows. I focus on delivering models that are practical, explainable, reliable, and measurable in real product environments.
I have worked across telecom and enterprise contexts, validating AI systems, developing ML-powered data pipelines, and collaborating with Data, Product, Business, and engineering teams. My work includes forecasting, capacity planning, churn prediction, computer vision, and customer-behavior analytics.
I use Python, SQL, FastAPI, Docker, cloud platforms, and modern MLOps practices to build production-ready solutions. I am experienced with model evaluation, drift detection, monitoring, structured logging, CI/CD, feature engineering, and automated workflows.
I also build generative AI systems using RAG, LangChain, LangGraph, vector databases, embeddings, and LLM evaluation frameworks. I apply strategic judgment to select appropriate AI approaches while balancing quality, latency, reliability, and explainability.
I am motivated by product-first AI work that turns data into actionable operational insights and helps teams make better decisions.
I define and execute AI model validation strategies for telecom decision intelligence systems at Inwi. I validate machine learning model performance, robustness, and reliability in production environments.
I monitor KPI quality and propose data-driven improvements. I collaborate with Data, Product, and Business teams to embed AI validation practices into product workflows.
I owned end-to-end development of ML-powered data pipelines, covering problem definition, feature engineering, model experimentation, production deployment, and monitoring. I built reliable data and feature pipelines using Python, REST APIs, and automated workflows.
I implemented drift detection and structured logging to improve production reliability. I also developed Power BI dashboards for decision intelligence KPI tracking and reduced manual data processing by 90% through intelligent automation.
I built and shipped computer vision models for road-sign detection and classification, progressing from prototype development to production deployment. The solution incorporated multi-source data fusion from camera, map, and sensor data.
I defined offline evaluation strategies, implemented model validation frameworks, and monitored deployed model performance using anomaly detection. This work supported reliable assessment of computer vision model quality in production.
I designed machine-learning-backed data transformation pipelines and customer behavior dashboards in a remote product environment. I supported the transformation of data into usable insights for business and product stakeholders.
I collaborated remotely with cross-functional product and business teams. My work combined data preparation, analytics, and dashboard development to improve decision-making.
Master, Mechanical Engineering, Robotics and Innovative Materials
Master's degree program in Mechanical Engineering, Robotics and Innovative Materials in Mohammedia, Morocco.
Data Science Certificate, Data Science
Thirteen-month Data Science certificate program with a GPA of 96/100.
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