From iantongs.tech — an encyclopedia of exactly one person, written by its subject.
Ian Tongs is a machine learning scientist. Normally this is where an encyclopedia would tell you where he was born and what he is known for, in a studiously neutral third person. But I’m writing this myself, so… I get to say this bit myself, in my own voice. Hi.
I’m an Australian, who for the past 5 years has lived and worked in America. I’m originally from Melbourne down in the south of Down Under, and since coming to America I have lived in Boston, Jersey city, and for the past 3 years now Philadelphia.
I work at Comcast, where I build data flows, machine learning models, and lately agentic systems and the retrieval layers underneath them. Through my work as a machine learning scientist, I get to practice my love for exploring the world through data, then making that data do things that impact the world for me. Before Comcast I optimized warehouses at Shopify, clustered alarms at Schneider Electric, modelled electricity markets while doing my masters at MIT, and analysed the reactions to marketing campaigns at Netwealth (Fintech asset management platform) back in Australia.
I set up this website to let me share my passion for data more outside of work, and to add some accountability for myself to actually do that exploring. If you are interested in seeing the world around you through the lens of science and analytics, then I hope my blog posts and projects will help inspire you too.
Experience
ComcastPhiladelphia, PA
Senior Machine Learning Scientist, Research, Analytics and Data Science2024 – present
Data Scientist, Enterprise Analytics and Data Science2023 – 2024
Designed and deployed a multi-agent forecasting system informing C-suite stakeholders on long-term strategic decisions, using supervisor-pattern orchestration with RAG retrieval and structured PDF outputs.
Built a stakeholder-facing research and synthesis agent, in beta internally, that lets users query company data and external sources directly and generates branded slide decks via a LangGraph StateGraph with conditional retry.
Built the RAG retrieval layer underpinning both systems — vector store chunking with MMR reranking — to mitigate hallucination and integrate enterprise knowledge sources.
Maintained end-to-end model deployment through a custom Databricks–GitHub–MLflow pipeline, contributing code to the pipeline itself, and held strict SLAs across a feature store feeding more than 200 production models.
Designed a prospect-targeting model suite supporting a monthly acquisition campaign of over $10M, including experimentation against incumbent approaches.
Shopify LogisticsRemote / Jersey City, NJ
Data Scientist, Node Optimization Team2022 – 2023
Developed a proof-of-concept optimization framework for targeted cycle counts, reducing inventory mismatches across the fulfilment network.
Rebuilt SKU-wise customer inventory reporting across Shopify warehouses, cutting unexplained changes in reported inventory by 90%.
MIT Sloan / Schneider ElectricCambridge, MA
Data Science Collaboration Partner2021
Analyzed over 30 million device alarm records, developing a two-stage approach using Word2Vec embeddings and adaptive clustering to reduce alarm volume by 65%.
MIT Operations Research CenterBoston, MA
Research Assistant to Professor Georgia Perakis2021 – 2022
Mined over 100 million pricing observations from US electricity market operators.
Formulated an optimization approach to electric vehicle and stationary storage purchasing decisions, improving renewable energy usage and customer savings.
Netwealth Asset ManagementMelbourne, Australia
Data Analyst, promoted from Marketing Analyst in 20202017 – 2021
Investigated causal factors behind customer churn and engagement, and presented intervention strategies to the board of directors.
Education
MIT Sloan School of ManagementCambridge, MA
Master of Business Analytics, Operations Research Center2021 – 2022
Masters program offered between the Operations Research Centre (course 15) and the school of computer science (course 6). Our coursework included classes in applications of machine learning, integer optimization, deep learning, and analytical methods, to name a few. Project work included examples like recommending prescribed lifestyle treatments from a 16,000-observation health survey using Optimal Policy Trees, and prototyping recommendation systems along the lines of what a music streaming service might use. I also completed a research assistantship under (the amazing) Prof. Georgia Perakis, getting to explore EV charge-discharge optimization in the electrical market alongside one of her talented PhDs.
Graduation in front of the Great Dome, circa 2022.
Monash UniversityMelbourne, Australia
BSc in Mathematical Statistics; BEc in Econometrics2017 – 2020
Monash Community Leadership Scholarship; Dean's Commendation List (Economics, top 5% of cohort); Dean's Award (Science, three years). Mentored underrepresented students through Access Monash.
Off the clock
Away from the models, I’m a nerd with a passion for exploring and tinkering. I love to Travel (in the past 5 years I’ve been to 14 countries… I think) and Hiking (especially when overseas) I also find very rewarding. I’m a passionate skier, especially when skiing in new and exciting places. I play Badminton casually with my friends, alongside board games and escape rooms (I know I know, a bit cliche for a tech nerd).
I’m a big history nerd, with a fondness for Roman history (learnt Latin in school - helluo librorum sum), which ties in well with my love of reading - history, sci-fi, even the odd bit of fantasy. Otherwise, you might find me at a musical or a play, or relaxing building one of the latest Lego sets.
Trekking in Patagonia, circa early 2025.Roman ruins in Morocco, circa early 2026.An MIT Sloan black-tie evening, circa early 2022.Les Misérables — Playbill dutifully collected, circa 2024.San Francisco's Chinatown, circa 2022.
