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autumninthecloud/README.md

Hi there 👋

Senior Technical Product Manager | AI, Data & Industrial SaaS

I'm a product leader specializing in AI, data products, B2B SaaS, and industrial technology, with 15+ years of experience spanning product management, data science, engineering, and commercial strategy.

I currently lead product at an AI-driven industrial SaaS company, where I established the company's first product management function and lead product strategy for industrial automation, observability, and sustainability solutions.

My technical foundation comes from a Master of Information and Data Science (MIDS) from UC Berkeley, complemented by earlier experience in engineering and global product management across water, utilities, manufacturing, energy, and industrial technology.

Beyond Product & Technology

I'm particularly passionate about using technology to make communities and built environments safer.

My interest in safety comes from both my engineering background and my belief that technology should solve meaningful, real-world problems—not just optimize metrics. I've worked on projects focused on building-code accessibility, safety compliance, data-driven community decision-making, and public-interest technology.

One example is CodeQuery, an AI assistant designed to make building codes easier to understand and improve safety compliance. I also care deeply about using data and technology to help communities make better-informed decisions about the places where people live and work.

This intersection—AI + product + safety + social impact—is a particularly meaningful part of the work I want to continue building.

What I Build

-AI and machine-learning products -Data analytics and decision-support platforms -B2B SaaS products and platforms -Industrial IoT and automation solutions -Customer-facing AI applications -Safety and compliance technology -Products that translate technical capabilities into measurable business and social outcomes

Repository Table of Contents

MIDS Coursework

-W210: Capstone - Influence Maximization and Fairness in Social Networks at Scale

-W231: Behind the Data

-W241: Causal Field Experiments

-W207: Machine Learning

-W203: Statistics

-W200: Programming with Python

-W209: Data Visualization - Tableau

-Data Viz Final project: Tracking Food Prices Around the Globe

Personal Advocacy

I've been researching and developing a product that utilizes AI and ML to identify building safety issues sooner. The product is called CodeQuery - check it out!

-UC Berkeley Calhacks AI/LLM hackathon - June 2023 - CodeQuery.AI

-CodeQuery.AI Devpost Project Page

Pinned Loading

  1. housing-agent-eval housing-agent-eval Public

    A Claude Code playground for evaluating agent architectures on housing safety analytics: single vs multi-agent pipelines, failure-mode analysis (MAST), and a lightweight governance audit mapped to …

  2. mike-khor/BobBuilderGPT mike-khor/BobBuilderGPT Public

    For CalHacks Hackathon 2023, team members: Mike Khor, Autumn Rains, Eric Dansforth, Meera Vinod

    Jupyter Notebook 2 1

  3. AIBillBrief AIBillBrief Public

    Python

  4. IM_w_fairness IM_w_fairnes 4C03 s Public

    Our group capstone project site for UC Berkeley's MIDS W210/Capstone.

    JavaScript 6

  5. Machine_Learning_W207 Machine_Learning_W207 Public

    Final Project for MIDS w207 machine learning course.

    Jupyter Notebook

  6. Olive_Oil_Experiment_W241 Olive_Oil_Experiment_W241 Public

    A causal field experiment to understand if product preference can be influenced based on marketing messaging. Final Project for MIDS w241.

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