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About me



  • 2023 - Present
    University at Buffalo

    Research Assistant (Data Science)

    • Performed in-depth analysis of wildfire data totaling approximately 5 GB. Merged information from diverse
    tables to identify counties most impacted, leveraging multiple variables for a comprehensive assessment

  • 2023 - Present
    Department of Energy University Sprint

    Data Scientist, The Opportunity Project

    • Collaborating with the U.S Department of Energy and the local authorities to develop a first ever two-way
    communication application, enhancing public-authority interaction and transparency
    • Conducted in-depth user-research interviews with emergency managers and a leader from the Red Cross, uncovering
    critical pain points and challenges faced during emergency response efforts; insights directly influenced product
    roadmap and feature development.
    • Created an interactive tableau dashboard for the emergency managers to view the public’s needs, and conducted
    analysis using tableau on power outage data.

  • 2023 - 2023
    RadicalX, New York

    Artificial Intelligence Engineer Intern

    • Behavioral analysis: Spearheaded the creation of the startup\’s inaugural fraud detection deep learning model,
    achieving an impressive 90% validation accuracy. Deployed the model in AWS SageMaker by utilizing AWS ECR to push
    the Docker image and create an endpoint from it.
    • Developed an AI manager using the Llama-2 7B model within the Langchain framework, replacing the paid OpenAI API

  • 2019 - 2022
    Tata Consultancy Services, Bengaluru

    Data Scientist

    • Anomaly detection: Developed and deployed an anamoly detection model to detect abnormal vibrational signal using
    autoencoder to encode the statistical properties and Kmeans to group signals
    • Reliability analysis: Successfully developed a proof-of-concept project involving the reproduction of a statistical model
    for the client, leading to the project being awarded to TCS
    o Consulted with a subject matter expert from engineering team and developed a rank regression model that matches
    the output of commercially available software for estimating machine reliability, resulting in a 60% reduction in costs
    associated with reliability estimation
    • Predictive analytics: Saved 20% in the cost involved in predicting a network failure for the client
    o Integrated data from multiple tables in Azure SQL Database using SQL, conducted data analysis, engineered new
    features, and developed a machine learning model to predict network failure on datasets exceeding 5 million records
    using azure databricks and effectively communicated analysis outcomes to the client using Tableau by crafting engaging
    data-driven narrative

  • 2018 - 2018
    LearnKart Technology, Bengaluru, KA

    Research Analyst

    • Customer Churn: Led a comprehensive customer churn analysis initiative for an online course, employing data-driven
    strategies to boost customer retention