Rajiv Sambasivan

Rajiv Sambasivan

Data scientist building enterprise apps

About

I am a data scientist. I began my career developing applications for scheduling and capacity management, I did that for about 10 years and then transitioned to data science in 2007 and have been doing it since then. In terms of formal training, I have a PhD in machine learning. In terms of domain expertise, I have developed applications for finance, telecommunications, retail, transportation, government and software tools. Translating data science to business impact is a challenge I really enjoy. Thank you for stopping by. Cheers.

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Maker History

  • GitHub
    GitHubStop losing data science context. Build knowledge graphs.
    May 2026
  • tseda
    tsedaUnderstand what is in your time series data
    Apr 2026
  • 🎉
    Joined Product HuntNovember 19th, 2025

Forums

GitHub - Stop losing data science context. Build knowledge graphs.

KMDS turns fragmented notebooks and data workflows into structured, searchable knowledge graphs. Scan repos using local LLMs, chat with your experimental history, and visually audit your data engineering artifacts—all 100% locally.

Introducing myself

Hi everyone!

I m Rajiv, and I ve been working on a project called tseda (Time Series Explorer).

If you ve ever stared at a messy time series dataset and wondered where to even start, I built this for you! It s an app designed to take the headache out of exploring and decomposing regularly sampled data (hourly or greater).

The "Why" behind it:
I wanted a tool that didn't just give me a raw plot, but actually helped me understand the structure of the data. tseda uses Singular Spectral Analysis (SSA) to break things down into Trend, Seasonality, and Noise, but it does the heavy lifting for you automating window selection and suggesting component groupings so you aren't just guessing.

tseda - Understand what is in your time series data

tool for exploring regularly sampled time series data - rajivsam/tseda See https://rajivsam.github.io/r2ds-... for a summary of features.
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