wrecklash.in
Abhilash Biradar
Data Scientist → Product Manager → Founder → AI
ex-Founder · ex-Sharechat · ex-ZET · UTS Data Science & Innovation · Sydney
From data science to product to founding a startup — now channeling that full-stack experience into AI.
About
Background
Started as a Data Scientist at Simpl, a BNPL fintech, then moved into product — leading content discovery at Sharechat (160M MAUs) and building data pipelines and a credit bureau rule engine as founding PM at ZET (Series A fintech, backed by General Catalyst & Sequoia). Went on to co-found STAD, a gaming community platform that reached 16K+ combined followers and 265K+ YouTube impressions.
That full arc — data, product, founder — now feeds into AI. Currently pursuing a Master of Data Science & Innovation at the University of Technology Sydney, focusing on machine learning and NLP. Published researcher — work on detecting depression in social media posts has 26 citations (Springer CCIS, 2019).
Education
Master of Data Science & Innovation
University of Technology Sydney
2025 – ongoing
B.Tech Computer Science
B.V.B Engineering College, Hubli
2014 – 2018
Experience
Work History
Co-Founder
STAD
Nov 2022 – Mar 2024
Bangalore
- Built a mobile app for 5K+ esports players to discover gamers and tournaments.
- Grew Discord community to 6K+ members; combined social following of 16K+.
- Partnered with S8ul and GodLike for YouTube live tournaments — 265K+ impressions, 4K subscribers.
Product Manager — Data & Experience
ZET
Nov 2021 – Oct 2022
Bangalore
- Founding PM at Series A fintech backed by General Catalyst, Nexus, and Sequoia.
- Built ETL/data pipeline for customer data points to enable strategic analytics initiatives.
- Led credit bureau partnerships; rule engine cut rejection rate by 16%, raised agent activation by 20%.
- Redesigned phased signup flow — 12% increase in install-to-signup conversion.
Associate Product Manager — Discovery
Sharechat
Mar 2020 – Nov 2021
Bangalore
- Led product discovery for India's largest vernacular social media — 160M MAUs.
- Revamped content discovery UI by user category consumption: +1% app retention, +3% topic consumption.
- New approval pipeline for trending topics: +50% trends adoption (5% of DAU), +12% section retention.
- Worked with external content partners to speed up content inflow and tweak feed logic — +9% time spent on trending topics.
- Documented A/B testing guidelines for accurate experimentation across product verticals.
Data Scientist
Simpl
Sept 2018 – Feb 2020
Bangalore
- Series B BNPL fintech — analysed growth prospects to secure 90% due collection in week 1.
- Deployed naïve Bayes classifier to categorise inbound SMS messages into Credit, Debit, and Loan.
Skills
Technical Stack
Languages
ML & Data
Tools & Infra
Methods
Projects
Selected Work
UTS Master of Data Science coursework: predicting nutritional deficiency diseases from patient clinical data across five diagnostic classes.
Self-initiated dashboard turning raw YouTube takeout data into consumption insights — watch history, content category mix, and engagement metrics.
End-to-end SQL/BI project on the Northwind Traders dataset — 10 business questions across sales, logistics, and pricing, with an interactive HTML report.
Writing
Articles & Case Studies
Unbundling: ShareChat's Path to Sustainable Growth
Exploring ShareChat's monetization challenges and opportunities — microtransactions and category-specific unbundling as strategies for India's digital economy.
NBA Fan Engagement: OTT Features
How the NBA can deepen fan engagement through OTT platforms — personalized content delivery and interactive viewing experiences.
India's Second-hand and Unbranded Market
Consumer behavior, market dynamics, and business opportunities in India's growing second-hand and unbranded goods sector.
MailChimp — SMS Feature
Exploring MailChimp's SMS marketing capabilities, integration possibilities, and customer segmentation strategy.
Should Fivetran Introduce Real-time Ingestion?
Technical challenges, business value, and competitive landscape for real-time ETL in Fivetran's data ingestion pipeline.
Publications
Research
Detecting Depressions in Social Media Posts Using Neural Networks
2019
Communications in Computer and Information Science book series (CCIS, volume 1037)
Contact
Get in Touch
Open to data science and product roles, and collaborations. Based in Sydney.