I'm a data scientist and AI/ML engineer based in Jakarta, Indonesia, working as an independent consultant for teams in fintech and consumer domains — helping them go from raw data to decisions that actually matter, through modeling, pipelines, and dashboards that non-technical stakeholders can use. I also contribute to NLP research on Southeast Asian languages in my spare time.
I studied Mathematics at Universitas Indonesia, graduating in 2023 — where I first realized that the most interesting problems live at the intersection of math, language, and human behavior. That conviction has shaped everything I've worked on since.
You can take a look at my experience on my LinkedIn here. · Open for consulting, freelance, and research collaborations — feel free to reach out.
Previously
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2025–now
Data Science Freelancer — Independent Consultant · working with clients across fintech, consumer, and research domains.
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2024
Data Analytics at Bank Saqu / Bank Jasa Jakarta (Astra & WeLab Group)
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2023–now
NLP Research Contributor at SEACROWD — building open-source NLP resources for Southeast Asian languages
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2023
Digital Consulting Analyst at Advisia
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2023
Product Operations Intern at Deall Jobs (YC 2022)
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2022–23
Data Scientist Intern at Home Credit Indonesia — project-based intern with Rakamin
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2022
Big Data Analytics Intern at Kimia Farma — project-based intern with Rakamin
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2022
Business Analyst Intern at DailySocial.id
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2021–22
Product Management Intern at SejutaCita (YC 2022)
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2019–23
🎓 B.Sc. Mathematics — Universitas Indonesia. Thesis on NLP & Twitter sentiment analysis of football fan bases.
Interests
I'm drawn to natural language processing — specifically how we can build models that understand the nuances of Southeast Asian languages, which are still deeply underrepresented in AI research. Working with SEACROWD made this feel less like an academic concern and more like something that actually matters for the hundreds of millions of people whose languages rarely appear in a training dataset.
I'm also genuinely interested in applied ML in finance: credit risk modeling, behavioral prediction, and how data can help build fairer systems for people historically excluded from financial services.
Lately I've been going deep into large language models — not just using them, but understanding how to build real systems around them. I've been working through a personal roadmap: starting from raw API calls and conversation memory, building up to RAG systems that let you talk to your own documents, then AI agents that can reason across tools and live data sources. The project I'm most excited about is a Fintech LLM Analyst — an AI that takes plain-English questions and translates them into SQL queries against real financial data. It sits right at the intersection of everything I care about: language, data, and decisions that affect real people. The goal is to have five production-ready LLM projects shipped, each one building on the last — all of it pointing toward the same question: what does it actually look like when AI works for the people using it.
Outside of Work
I play a lot of games — and I mean a lot. My range is embarrassingly wide: one moment I'm sweating in CS or grinding ranked in MLBB, the next I'm in Roblox without a hint of shame. A big part of what I play these days is honestly influenced by Windah Basudara — if he touches it, there's a good chance I end up trying it. I also watch a fair amount of gaming streams, mostly his. It's the kind of content that makes you want to close your laptop and just play.
I watch anime. My favorite is One Piece — I've accepted that it will outlive most of my life decisions. I don't know when it ends, but I already know I'll be a little sad when it does. My favorite character is Buggy, which I think says a lot about me and I'm fine with that. Outside the big three, the shows that genuinely stuck with me are Frieren, Saiki K, and Bocchi the Rock — three very different things, somehow all comfort watches.
I also follow football. I support Manchester United, which at this point is less a hobby and more a test of character. We're not great. We've been not great for a while. But here we are.
Projects
Selected Work
Fintech Segmentation API
A production-ready REST API serving a KMeans behavioral segmentation model trained on 278,932 transactions from an Indonesian digital payment platform. The model classifies users into 4 segments — Gaming Casuals, Active Explorers, Established Regulars, and Food-Focused Occasionals — across 31 behavioral features. Fully Dockerized with a CI/CD pipeline via GitHub Actions and deployed live on Railway.
Fintech Behavioral Segmentation
The research side of the segmentation work — customer clustering on fintech transaction data to identify distinct behavioral patterns. Feature engineering across spending habits, merchant diversity, temporal patterns, and payment behavior, feeding into the KMeans model that eventually became the deployed API above.
Olist Analytics — dbt + BigQuery
An end-to-end data pipeline on the Olist Brazilian e-commerce dataset, modeled into a proper star schema with staging, intermediate, and mart layers. Built to practice production data modeling standards — the same BigQuery mart tables that feed into the AI agent project currently in progress.
Face Recognition — Computer Vision
A real-time face recognition system built from scratch, covering face detection, face matching, and identity classification. One of my earlier projects — the point where I first realized that making a computer see things was way more interesting than I expected.
SEA Dataset Dataloader — SEACROWD
Open-source contribution to the SEACROWD initiative — building standardized HuggingFace dataset dataloaders for Southeast Asian language datasets, alongside dataset documentation covering licensing, size, language and dialect metadata, and annotation methods. This work is part of the broader research effort that led to the SEA-VL paper at ACL 2025.
Earlier Work
Twitter Sentiment Analysis — Football Fan Bases
Comparative sentiment analysis of Indonesian Twitter discourse around Real Madrid and FC Barcelona fan communities. Applied text preprocessing, lexicon-based methods, and ML classifiers on scraped Twitter data.
Stock Trading Robot
An automated stock trading agent built for a mathematical modeling course. Uses LSTM to predict stock prices up to ten days ahead and recommends whether to buy, sell, or hold. Includes a full written report and presentation decks.
Awards, Research & News
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2025
Research SEA-VL: Multicultural Vision-Language Dataset for Southeast Asia
Co-author. Cahyawijaya et al., ACL 2025 (Long Papers). Contributed to dataset preprocessing and documentation. [ACL Anthology] -
2023
Award Most Outstanding Student — Mathematics Dept., UI
Recognized out of 600+ students. -
2022
Award 1st Runner Up — ASEAN Data Science Challenge 2022
Proposed GetHired, a platform for youth employment readiness. Competed against 200+ teams. -
2022
Award 1st Place — Ganesha Student Innovation Summit Challenge 2022
Outperformed 100+ individual participants and 10 finalist teams. -
2021
Award 1st Winner — International Youth Summit for Renewable Energy 2021
Waste to Energy Challenge. Awarded USD 1,800. Outperformed 200+ participants. -
2021
Scholarship Dato' Dr. Low Tuck Kwong Scholarship