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Hi, I’m Hassan Muhammad Siraj Zafar, a data scientist based in Napoli, Italy. I’m completing an MSc in Data Science with a focus on Big Data, Data Mining, and Statistics, and I bring over two years of hands-on industry experience in machine learning, MLOps, and advanced analytics. I’ve deployed scalable ML solutions, built automated pipelines with Docker and Kubernetes, and improved model accuracy through hyperparameter tuning and drift detection. I also work with LLM-based applications, automated data workflows, and data-driven storytelling to turn large datasets into actionable insights. I’m passionate about applying AI to real-world problems and helping teams make data-informed decisions.…Hi, I’m Hassan Muhammad Siraj Zafar, a data scientist based in Napoli, Italy. I’m completing an MSc in Data Science with a focus on Big Data, Data Mining, and Statistics, and I bring over two years of hands-on industry experience in machine learning, MLOps, and advanced analytics. I’ve deployed scalable ML solutions, built automated pipelines with Docker and Kubernetes, and improved model accuracy through hyperparameter tuning and drift detection. I also work with LLM-based applications, automated data workflows, and data-driven storytelling to turn large datasets into actionable insights. I’m passionate about applying AI to real-world problems and helping teams make data-informed decisions.

Hassan Muhammad Siraj Zafar

Data Scientist, AI Engineer, Programmer, +6

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Hi, I’m Hassan Muhammad Siraj Zafar, a data scientist based in Napoli, Italy. I’m completing an MSc in Data Science with a focus on Big Data, Data Mining, and Statistics, and I bring over two years of hands-on industry experience in machine learning, MLOps, and advanced analytics. I’ve deployed scalable ML solutions, built automated pipelines with Docker and Kubernetes, and improved model accuracy through hyperparameter tuning and drift detection. I also work with LLM-based applications, automated data workflows, and data-driven storytelling to turn large datasets into actionable insights. I’m passionate about applying AI to real-world problems and helping teams make data-informed decisions.…Hi, I’m Hassan Muhammad Siraj Zafar, a data scientist based in Napoli, Italy. I’m completing an MSc in Data Science with a focus on Big Data, Data Mining, and Statistics, and I bring over two years of hands-on industry experience in machine learning, MLOps, and advanced analytics. I’ve deployed scalable ML solutions, built automated pipelines with Docker and Kubernetes, and improved model accuracy through hyperparameter tuning and drift detection. I also work with LLM-based applications, automated data workflows, and data-driven storytelling to turn large datasets into actionable insights. I’m passionate about applying AI to real-world problems and helping teams make data-informed decisions.

Available to hire

Hi, I’m Hassan Muhammad Siraj Zafar, a data scientist based in Napoli, Italy. I’m completing an MSc in Data Science with a focus on Big Data, Data Mining, and Statistics, and I bring over two years of hands-on industry experience in machine learning, MLOps, and advanced analytics.

I’ve deployed scalable ML solutions, built automated pipelines with Docker and Kubernetes, and improved model accuracy through hyperparameter tuning and drift detection. I also work with LLM-based applications, automated data workflows, and data-driven storytelling to turn large datasets into actionable insights. I’m passionate about applying AI to real-world problems and helping teams make data-informed decisions.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Language

English

Fluent

Italian

Beginner

Urdu

Fluent

Work Experience

Data Scientist II at Afiniti

October 1, 2023 - October 15, 2025

Optimized machine learning models for customer interaction predictions, improving prediction accuracy by 15–20%. Developed MLOps pipelines using Docker, reducing deployment time by 40%. Conducted data analysis and feature engineering, increasing model performance (AUC / F1) by 25% through better input variables and reduced noise. Streamlined data collection methods, reducing data processing errors by 30% and cutting preprocessing time by 50%. Automated repetitive tasks using Python and R, saving 10+ hours per week and enabling faster delivery of insights and reports.

Data Scientist I at Afiniti

December 1, 2022 - October 15, 2025

Implemented drift detection and model retraining pipelines for real-time prediction systems, reducing model degradation and maintaining over 90% prediction reliability in production. Built automated model performance monitoring tools, enabling early detection of underperforming models and cutting incident response time by 60%. Analyzed large-scale datasets (100M+ records) to generate actionable insights, improving AI-driven solution outcomes by 20–30% through better feature targeting and system tuning. Evaluated emerging technologies to assess potential applications within the organization’s existing infrastructure or future projects. Conducted feature engineering efforts to enhance model performance by creating new relevant variables from raw input data sources.

Data Engineer at Afiniti

February 1, 2022 - October 15, 2025

Built and maintained robust data pipelines supporting machine learning workflows, reducing data processing time by 35%. Developed and optimized ETL processes using SQL and Python, improving data throughput and reducing query execution time by 40%. Assisted in transitioning machine learning models from research to production, decreasing deployment time by 50% and ensuring production readiness. Collaborated on ETL tasks to ensure data integrity and pipeline stability, maintaining 99.9% pipeline uptime. Fine-tuned SQL queries and optimized database structures, resulting in 1.5x - 2x faster data retrieval and improved reporting accuracy by 25%.

Junior Software Engineer at Dream Big Semiconductor

September 16, 2020 - May 5, 2021

Developed multiple Linux kernel modules using C, demonstrated proficiency in Makefiles and low-level system interactions. Implemented a command interception module to hook IP xfrm user commands, enabling IPsec offloading in a proprietary hardware-integrated module, boosting data throughput by up to 60%. Optimized performance by aligning kernel modules with specialized hardware, significantly enhancing data rates and system efficiency. Diagnosed and resolved code defects and system failures, contributing to a cleaner architecture and more maintainable codebase. Delivered production-ready features and modules on time in a fast-paced R&D environment.

Data Scientist at Power Improvement (PI) Limited

July 1, 2025 - September 1, 2025

Internship period where I built and deployed three LLM-based chatbot versions (T5, LLaMA 3, Mistral) and achieved 25% higher response accuracy and 40% lower latency using Groq’s API. I developed automated web-scraping pipelines to enhance data coverage by 30%, implemented semantic search with SentenceTransformers and FAISS to improve retrieval relevance by 35%, and integrated RAG workflows with HuggingFace embeddings for up-to-date responses. I designed a Streamlit dashboard with a model-selection interface to streamline testing and model comparisons by 50%, and optimized pipelines via caching, embedding refinement, and prompt tuning to maintain performance consistency across versions.

Data Scientist II at Afiniti

December 1, 2022 - October 1, 2023

Optimized ML models for customer interaction predictions, improving accuracy by 15–20%. Built MLOps pipelines using Docker to reduce deployment time by 40% and enable seamless production integration. Conducted data analysis and feature engineering to boost model performance (AUC/F1) by ~5% and streamlined data collection, cutting data processing errors by 30% and preprocessing time by 50%. Automated repetitive tasks in Python and R, saving 10+ hours per week.

Data Engineer at Afiniti

May 1, 2021 - February 1, 2022

Maintained robust data pipelines supporting ML workflows, reducing data processing time by 35%. Optimized ETL processes using SQL and Python to improve throughput and reduce query times by 40%. Assisted in transitioning ML models from research to production, decreasing deployment time by 50% and ensuring production readiness. Collaborated on ETL tasks to ensure data integrity and pipeline stability with 99.9% uptime. Fine-tuned SQL queries and DB structures, yielding 1.5x–2x faster data retrieval and 25% improved reporting accuracy.