Kanav Arora is a data scientist with six years of experience building production-ready ML and data engineering solutions at Deloitte, currently seconded to the US global data science team. He designs agentic AI pipelines and deep RAG systems using LangChain, LangGraph and FAISS, and has a proven focus on retrieval precision and pipeline performance for scalable real-world use cases. Early career wins include a best paper award for research on autism spectrum disorder detection and selection for Amazon ML Summer School, reflecting a blend of research rigor and applied engineering. A hands-on contributor to open-source front-end work (notably UI improvements on the modern CRM alternative "twenty"), he pairs strong full-stack instincts with competitive programming discipline and a lifelong appetite for learning.
6 years of coding experience
1 year of employment as a software developer
Bachelor's degree, Computer Science, 9.4, Bachelor's degree, Computer Science, 9.4 at Bennett University
Intermediate, Non - Medical, 94.7%, Intermediate, Non - Medical, 94.7% at B.C.M Arya Model. Sr. Sec. School
Building a modern alternative to Salesforce, powered by the community.
Role in this project:
Front-end Developer
Contributions:29 reviews, 39 PRs, 265 comments in 5 months
Contributions summary:Kanav primarily contributed to the front-end development of the "twenty" project, focusing on the user interface. They implemented new object type sections, added cancel and save buttons to object field steps, and created an object field edit page. Additionally, they addressed UI issues like layout shifts in date inputs, corrected tag spacing, and styled shortcut keys, improving the overall user experience. The contributions involved modifications to React components using TypeScript, and integrating with UI libraries.
Contributions:10 reviews, 53 PRs, 96 pushes in 6 months
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