Waqas Ahmed

I’m a Data Scientist and Machine Learning Engineer focused on building end-to-end analytics and ML systems that deliver measurable business impact. My work spans predictive modeling, forecasting, NLP, and AI-driven decision support, with an emphasis on turning raw, messy data into reliable, actionable outputs.

I’ve worked as a Data Science Intern at AI-GenMat (Jun 2025 – Aug 2025), where I contributed to Retrieval-Augmented Generation (RAG) pipelines for AI reasoning workflows, built reusable Python data preprocessing pipelines that reduced manual processing time by ~30%, and supported the evaluation of machine learning and neural network models to speed up experimentation cycles.

Beyond my internship, I’ve designed and implemented multiple real-world analytics systems, including:

• A Formula 1 race outcome prediction system achieving MAE ≈ 2.4 positions (~35% better than baseline heuristics)
• Sales and demand forecasting platforms improving forecast accuracy by ~20%
• Customer churn and lifetime value models identifying high-risk users with ~75% precision
• Automated analytics and ML platforms reducing analysis turnaround time by ~60%
• Financial risk and credit scoring systems improving risk classification accuracy by ~18% with explainable outputs

My technical stack includes Python, SQL, Power BI, Scikit-learn, XGBoost, TensorFlow, PyTorch, NLP frameworks (spaCy, Hugging Face), LLM systems (RAG, FAISS, OpenAI API), and deployment tools like Streamlit, FastAPI, Docker, and GitHub Actions.

I’m available for data analysis, business intelligence dashboards, machine learning model development, forecasting, NLP solutions, AI workflow integration, and analytics consulting, with a strong focus on clarity, reproducibility, and real-world usability.