Ali sedghiye

Gathered requirements with the client, designed the architecture, built the web app and deployed it to production. Used AI-native development with Claude Code and other AI coding agents, then reviewed and tested their output.

Worked with the CEO and executive team to define a fault-detection project and reported progress and results. Built Python pipelines that collected sensor data from 3 production lines to monitor main operational KPIs, and applied anomaly detection and classification models to catch faults earlier.

Served as single point of contact for customers, ran requirement meetings, clarified needs, and agreed scope and timelines. Ran customer demos, handled change requests, and stayed their contact after handover.

Built ML models for production scheduling, contributing to a 20% reduction in downtime (about 40 hours saved per month). Analyzed 2+ years of production data and automated weekly KPI reporting across 8 production stages.

Built NLP text-classification pipelines (TF-IDF, Scikit-learn) and web-scraping pipelines that replaced manual data collection.

Pursuing an MSc in Data Science at the University of Milano-Bicocca, expected to complete in 2026.

Completed a BSc in Industrial Engineering at Iran University of Science and Technology, with a thesis on data governance and defect detection in process systems.

Completed the Deep Learning Specialization offered by DeepLearning.AI.

Completed the Data Analysis with Python course offered by IBM.

Completed the Introduction to Statistics course offered by Stanford University.