About me
š Hi, Iām Youssef, a data scientist with a strong product mindset, currently working in a product-based company where I design and implement end-to-end machine learning solutions that directly impact core features.
I specialize in deeply understanding business needs and product constraints such as latency, inference cost, user experience, and scalability to design ML systems that are not just accurate, but also practical, maintainable, and aligned with product strategy.
My approach ranges from developing fast, interpretable baselines to implementing state-of-the-art models informed by cutting-edge research. I am able to bridge the gap between experimentation and delivery translating ambiguous product requirements into scalable, robust systems used in real-world environments.
My areas of expertise and the tools I work with include:
Machine Learning & Deep Learning: Information Retrieval, NLP, time series, text embeddings, recommender systems.
Product-Focused ML: System Design, Latency-aware modeling, experimentation frameworks, metrics alignment.
ML Engineering: Model deployment (batch & real-time), APIs, pipelines, CI/CD, monitoring.
Applied Research: Benchmarking, reading & adapting and publishing academic papers / technical blogs, prototyping SOTA methods.
Tools & Ecosystem: Python, PyTorch/Tensorflow, scikit-learn, Airflow, FastAPI, Docker, MLflow, Spark, AWS.