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justiwriter

Justin Babu

@justiwriter

Data Scientist and AI developer

India
English, Hindi, Malayalam, Tamil
About me
I'm a data science professional with a Master's degree from IIT Kanpur, where I graduated with a gold medal. I help businesses turn raw data into actionable insights and build practical solutions using Python, machine learning, statistical analysis, and applied AI. From data cleaning and visualization to predictive modelling and AI-powered automation, I focus on delivering reliable results, clean code, and solutions tailored to your business needs. My goal is to make complex problems simpler and deliver work that is accurate, useful, and easy to understand.... Read more

Skills

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justiwriter
Justin Babu
Offline • 
Average response time: 2 hours

See my services

Data Science Consultation
I will analyze your data and build machine learning models
From$50 / projectMore details

Portfolio

Work experience

Goods_And Services Tax Network

Assistant Manager

Goods And Services Tax Network • Full-time

Jun 2025 - Present • 1 yr 4 mos

•Built and benchmarked three Text-to-SQL architectures (LLM baseline, domain-specific RAG, and multi-agent framework) with query planning and schema grounding over a GST analytics database — RAG improved lenient accuracy from 56% to 64% and reduced the silent-wrong rate from 42% to 32%; evaluated across 39 gold queries on execution rate and result correctness. •Measured the incremental revenue effect of national policy changes using a Difference-in-Differences (DID) framework — benchmarking firm-level fixed-effects regression against an XGBoost counterfactual trained on pre-intervention data across 20,000+ accounts, isolating a statistically significant 10.6% uplift (p = 0.05) in one category against a null result in another. •Modelled the link between tax collections and macroeconomic indicators (GDP and GVA) using SARIMAX, with two-quarter-ahead GDP forecasts landing within 2.96% of published actuals. •Built scenario and sensitivity models tracing how rate changes flow through an 11,283 × 11,283 Input-Output matrix, quantifying revenue impact across 1,399 product-rate combinations. •Built an end-to-end semantic search pipeline (Product Search Engine) using sentence transformers (multilingual-e5-small) mapping free-text product descriptions to tax codes across 150 million records — achieving 72% top-5 accuracy against a 700-query benchmark, outperforming the existing keyword-search tool.

KPMG

Analytics Intern

KPMG • Part-time

Jun 2025 - Aug 2025 • 2 mos

Built six-week-ahead electricity demand forecasts for a Central Electricity Authority engagement, comparing SARIMA, ARFIMA, Holt-Winters and Ridge/Lasso specifications on forecast accuracy. •Benchmarked machine learning alternatives: gradient boosting, random forest, SVR, GAMs and neural networks, against the econometric baseline over the same horizon rather than committing to a single method. •Reviewed published forecasting research across short, medium and long horizons to establish which approach suited each, informing model selection for the engagement