Sidhantha Poddar

When I was just seven years old, I discovered the world of computers and I was immediately hooked. I loved exploring everything about them, from writing simple calculators to understanding how peer-to-peer systems worked for downloading data from the internet. As I got older, I started delving deeper into the world of technology, exploring cybersecurity, machine learning and development.

In college, my focus shifted from just learning new technologies to problem-solving and finding the best solutions for specific problems. It was during a college hackathon that I was first introduced to blockchain. I developed an e-voting system for Indian elections using ATM machines and biometric validation, using Gnache to locally host the Ethereum blockchain and Metamask as the signing wallet.

It was during this project that I fell in love with blockchain technology. From then on, I dedicated myself to learning more about web3 development and how to balance the use of both centralized and decentralized systems to achieve the best outcome.

Today, I work as an Analyst Consultant for McKinsey, where I provide analysis and develop systems and architectures for MNCs. I believe that the combination of AI, Web3, XR, and advancements in computing such as quantum and analog computing will revolutionize the world in major ways, and I am determined to be a part of that change.

Projects

Chimère Pay

Matic Payments using Mobile Phone Numbers.MetaMask, JavaScript, Infura, Flask, Docker, Python, AWS, Web3, Bootstrap, Polygon

NeuraNFT

NeuraNFT: Tokenizing intelligence and empowering individuals with personalized, secure AI through blockchain-based ownership.Solidity, Flask, Docker, Python, React.js, Llama, Retrieval Augmented Generation (RAG), Federated Learning, Quicknode Streams, QuickNode Functions

ChainConnect

Bridging Communities through Decentralized CollaborationReact, Flask

Skills

Python

Cloud

web3

Data Science

Full Stack

Experience

McKinsey & Company - Analyst
July 2021 - Present
GE Aviation - Data-Science Intern
March 2021 - July 2021
1. As part of my work on a time-series analysis model, I focused on detecting engine failures in aircraft engines using data from over 1000 sensors.
2. By optimizing various aspects of the model, I was able to improve its speed by 5 times and increase the F1 score by between 4% and 9%. Additionally, I was able to reduce the number of false negatives generated by the model.
3. In order to streamline the process and reduce the turn-around time, I implemented automation for multiple steps in the analysis.
4. To enhance the analytics capabilities of the model, I added data visualization components to enable a more effective representation of the results.
5. Building on this foundation, I developed a prediction framework using deep learning techniques to predict engine failures more accurately. This framework was integrated into the existing time-series analysis model.
Datanest Ventures - Founder
January 2020 - March 2021
1. Our team of 3 founders was able to secure funding from The Meta Group, a European investment group, for our project, Covifight.
2. In addition to the funding, we received valuable resources in the form of AWS credits and Wolfram Alpha Pro credits, totaling $6,500 and $1,875 respectively. These credits were used to support the deployment of our project.
3. Covifight is a hybrid mobile and web application that was developed using a range of technologies, including Java, Python, Node.js, and Gremlin Graph DB. The app was deployed using Kubernetes on Azure.
4. One of the key features of Covifight is its ability to significantly improve the detection rate of Covid-19 through contact tracing. Our app was able to achieve a 3 times improvement compared to the existing Decentralized Privacy-Preserving Proximity Tracing (DP3T) model.
5. As the tech-lead of the Covifight team, I was responsible for managing a group of 12 developers. I also contributed to the development of machine learning models and the database schema, and served as the Scrum Master to facilitate communication and collaboration between cross-functional teams.
6. In order to promote the adoption and success of our app, I had the opportunity to meet with a diverse group of partners and stakeholders, including public officials, academic institutions, corporate partners, and venture capitalists. These meetings were held across Europe and involved more than 50 individuals in total. We were able to discuss how Covifight could be used to boost the detection of Covid-19 patients and provide support to various countries during the pandemic.