Hi, I'm Ali Zarreh

Senior Data Scientist | AI/ML Engineer | Tech Leader

Welcome to a world where data unlocks endless possibilities. As a Senior Data Scientist with over a decade of experience, I specialize in transforming complex business challenges into elegant AI-powered solutions. My expertise spans from building scalable machine learning systems to architecting cutting-edge generative AI applications.

I've led cross-functional teams in developing enterprise-grade forecasting engines, optimization platforms, and intelligent document systems that have driven millions in business value. Whether it's reducing forecast errors by double digits, cutting computational costs by 70%, or building next-generation RAG systems, I'm passionate about leveraging data science to create tangible impact.

Currently focused on the intersection of LLMs and enterprise applications, I'm exploring how generative AI can revolutionize how we interact with data and automate complex workflows.

Skills

Programming Languages & Development

Proficient in Python, SQL, Scala, JavaScript, and R, with a focus on application in data science and software development. Skilled in writing efficient, clean, and maintainable code for complex projects.

Machine Learning & Deep Learning

Expert in deploying large-scale machine learning and deep learning models using advanced frameworks like TensorFlow, PyTorch, and XGBoost. Experienced in creating predictive models that drive actionable insights.

Big Data Technologies

Experienced in handling vast datasets using technologies like Apache Spark, Hadoop, and Ray. Expertise in designing scalable data processing pipelines to support business intelligence and analytics.

LLM & Generative AI

Expert in designing and deploying LLM-based systems including RAG architectures, multi-agent workflows, and prompt engineering. Experienced with LangChain, LangGraph, and foundation models (GPT-4, Llama, Claude) for building intelligent document assistants and conversational AI solutions.

Cloud Computing & DevOps

Proficient in deploying scalable applications on cloud platforms like AWS, GCP, and Azure. Skilled in managing CI/CD pipelines and implementing DevOps practices to ensure efficient development workflows.

Optimization & Forecasting

Specialized in applying optimization techniques and forecasting methods using advanced mathematical models to improve efficiency and accuracy in business operations and decision-making.

Projects

GenAI Docs Helper: Intelligent Document Assistant

A production-ready agentic RAG system leveraging LangGraph state machines and advanced AI workflows for intelligent document retrieval. Features sophisticated hallucination detection, multi-strategy retrieval with confidence scoring, and enterprise-grade performance optimization achieving 92% retrieval accuracy and 98% factual accuracy in responses.

GenAI Job Finder: AI-Powered Career Discovery Platform

A sophisticated conversational AI platform leveraging LangChain and LangGraph state machines to create intelligent career guidance experiences. Features multi-LLM architecture (GPT-3.5, Llama 3.2), advanced prompt engineering, and an interactive AI assistant that provides real-time personalized career advice through natural language conversations.

Forecasting Engine for Retail Analytics

Developed a highly scalable and accurate demand forecasting engine utilizing Ray distributed computing framework and Vertex AI on Google Cloud Platform. This enterprise-grade solution handles complex retail scenarios across multiple product categories and promotional strategies, featuring advanced machine learning workflows, BigQuery integration for extensive preprocessing, and parallel processing capabilities that deliver exceptional forecasting accuracy and operational efficiency.

Car Insurance Telematics Risk Assessment

Developed a comprehensive machine learning system to predict insurance claim probability and severity using telematics data. Features include advanced feature engineering, XGBoost modeling with Optuna hyperparameter tuning, and a modular inference pipeline.

PipeLearner: Machine Learning Optimization Tool

Created PipeLearner, a Spark distributed package that cuts down model training and deployment time by over 50%, streamlining the process and enhancing productivity across data teams.

Optimization of Promotional Forecasting Platforms

Transformed the promotional forecasting platform at a retail company to a PySpark distributed computing framework, revising logics and models in AWS. This led to a 2% reduction in error and a significant cut in runtime by nearly 70%.

Warehouses Orders Forecasting Challenge

Developed a solution for predicting grocery delivery orders at Rohlik warehouses across Europe, enhancing workforce allocation, delivery logistics, and inventory management through accurate forecasting.

MMA Fighter Data Analysis and Ranking

Conducted a comprehensive analysis of MMA fighters using data scraping, ranking, and visualization techniques. This project provides insights into fighter performance and serves as a tool for fans and analysts.