Hi, my name is
Jayant Dabas.
I turn data into actionable insights and create robust applications.
About Me
Intro
Hi, I'm Jayant Dabas, the founder of AnalyticsQ. I'm currently pursuing a graduate degree in Data Science at New York University (NYU), learning in-depth knowledge to improve the application. In my previous roles, I've worked as a Software Engineer, DevOps engineer, and Data Analyst while collaborating with diverse teams in multiple regions in APAC.
Life Story
My journey in data science began with a passion for uncovering insights and solving real-world challenges. From building model rockets in school to analyzing global event data in university, I've always been driven by curiosity, diversity, and resilience.
Today, I'm inspired by the transformative power of data and technology to create meaningful impact and advance societal progress. Welcome to my digital space, where I share my journey, projects, and aspirations in the ever-evolving world of data science.
Free Time
I cherish the wonders of nature, love to play soccer, enjoy the joy of cooking, and love dogs.
My Experience
Herndon, VA
May 2025 ー Aug 2025
Software Development Engineer Intern
New York, NY
Mar 2025 ー Present
Graduate Research Assistant - Causal Inference in LLMs
- Evaluate Large Language Models (LLMs) for causal reasoning in categorization tasks.
- Design and execute experiments comparing LLM-based reasoning to human causal categorization judgments.
- Assess LLMs' ability to infer causal structures by analyzing results against Bayesian network models.
- Query LLM APIs and fine-tune models to test their causal reasoning capabilities under different experimental conditions.
- Collaborate with faculty to synthesize findings and contribute to research publications.
Nov 2024 ー Present
Graduate Research Assistant - Data Science
- Developed predictive models using Python and regression analysis to quantify the impact of economic development, resource access, and electoral competitiveness on gender disparities in voter turnout.
- Engineered a data pipeline to process and merge large-scale voter datasets, reducing preprocessing time by 40%.
- Visualized trends in gender-based voting behavior using Matplotlib and Seaborn, improving stakeholder insights.
Tokyo, Japan
Oct 2022 ー Jul 2024
Data Scientist, AI Engineer
- Developed AI automated pipeline for enhancing second-hand item listings on Rakuma using Azure OpenAI API to extract missing attributes across 5 target genres, improving metadata extraction accuracy to 92%.
- Designed and deployed 30 Apache Airflow DAGs to automate critical processes, reducing manual intervention.
- Integrated NLP based scoring model into Rakuten's e-commerce reviews, improving user engagement rates.
- Collaborated with cross-functional teams to implement data pipelines, ensuring seamless data flow across apps.
- Optimized Google BigQuery storage by identifying and decommissioning unused project tenant data, reducing storage costs by $4,000 per month and improving query performance.
New Delhi, India
Aug 2020 ー Jun 2024
Founder and Director of Technology
- Led a diverse team of 5+ professionals to design and develop AnalyticsQ, an enterprise-level data analytics tool that optimizes data processing, saving time and resources by 60%.
- Secured $100,000 in seed funding to scale the product, expanding its adoption to 100+ customers.
- Directed project timelines, developed features, and managed business team to ensure successful delivery.
- Built predictive features, enabling businesses to identify revenue opportunities with 20% higher precision.
Kowloon, Hong Kong
Sept 2020 ー May 2021
DevOps Engineering Intern
Bank of East Asia - Hong Kong
- Automated application pipelines for IBM Urban Cloud to allow 32% quicker and more efficient releases.
- Discovered and enhanced cloud security frameworks to address privacy issues and ensure data integrity.
- Analyzed performance metrics to identify bottlenecks, optimizing system performance by 15%.
Kowloon, Hong Kong
Sept 2019 ー Apr 2021
Research Assistant - Active Learning
- Supported data collection and prepared experiments for Dr. Xueying Zhan's research on active learning models, contributing to the paper "A Comparative Survey of Deep Active Learning" (Zhan et al., 2022).
- Conducted data analysis to assist in the evaluation of models, identifying patterns to support research findings.
Apr 2019 ー Apr 2020
IoT and Mobile App Developer
- Designed and developed the official CityU mobile app in Flutter with 120K+ downloads to enable student access.
- Implemented APIs with MuleSoft, tested with Postman, and used JIRA for Agile development.
Apr 2019 ー Jun 2019
Data Analyst
- Developed an end-to-end ETL pipeline to extract, transform, and analyze 50 years of geopolitical and social event data from Google's GDELT dataset.
- Implemented parallel processing in Apache Spark, reducing data processing time by 36 hours.
Things I've Built
Latural Language Understanding - New York University, New York, NY
- Evaluated state-of-the-art LLMs on their ability to process and retain complex causal relationships over time.
- Introduced a Real-Time Adaptation Challenge to assess LLMs' reasoning accuracy under dynamic scenarios.
Cryptography - Web applications
- Published an open source lightweight, standalone password hashing library with zero external dependencies.
- Compatible with Node.js, Next.js, and most browsers, providing a simple API similar to bcrypt.js.
- Available for use at https://www.npmjs.com/package/bcrypt-mini
Research thesis - City University of Hong Kong, Hong Kong
- Wrote a research paper on a zero-shot learning method used in unfamiliar object classification in autonomous vehicles, NLP, or detection of unforeseen diseases in medical imaging such as X-rays
- Employed generative approach with text captions to creatively extend the model to new domains
MiraiDensei Ltd - India
- Led a team of motivated and qualified people with various skills to develop AnalyticsQ, an enterprise-level automation tool that uses real-time analysis and interactive pipelines to save time and resources by 60%
- Developed the platform using technologies like React, Node, Express, PostgreSQL, Python, and more
- It received seed funding of HK $100,000 from Hong Kong Science and Technology Park in 2021
Data Intensive Computing - City University of Hong Kong
- Analyzed COVID-19 news articles of the past 2 years to identified its propagation and impact worldwide
- Presented the results by building a visualizer using Apache Spark, Python, and JavaScript to analyze the big data from the GDELT dataset provided by Google
- The Data Extraction using Spark significantly improved resource allocation improving the CPU performance by 36% and the memory performance by 14%
City University of Hong Kong - Hong Kong
- Developed CityU Mobile, which is an official mobile app of City University of Hong Kong
- It is one of the top-rated University apps in Hong Kong, with over 100,000 downloads each term
- Implemented internal authentication APIs with MuleSoft and tested with Postman
04.What's Next?
Get In Touch
Although I’m not currently looking for any new opportunities, my inbox is always open. Whether you have a question or just want to say hi, I’ll try my best to get back to you!
Say Hello