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About me



  • 2022 - 2023

    Data Scientist

    • Spearheaded a project to predict the repeat repair rate, cost per claim, parts used for service orders of machines leveraging statistical techniques and Machine learning models for all Lenovo brands across globe.

    • Transmuted the findings for all KPIs through Power BI dashboard.

    • Worked in Topic modelling for unstructured data with customer service requests.

    • Worked in Geo-spatial analysis with customer install base.

  • 2019 - 2020
    Fidelity Investments


    • Managed a project of forecasting for demand hours and volumes of resources attending clients for Defined benefit plan which helped the managers to allocate their respective team resource accordingly. Resource planning
    helped to brought down the hiring cost by 50%.

  • 2016 - 2019


    • Led a project in workforce labor forecasting and Optimization to forecast hotel traffic for a hospitality management company using traditional Time- Series and Machine-learning models, predicting human traffic.

    • Gathered internal and external predictor variables, performed all the EDA, scraped weather and events data.

    • Built multivariate Time-Series models with Arima-x, UCM and Ensemble methods of all bagging, boosting models
    to achieve an accuracy of 98% at highest level and granular level accuracy of 94% on average.

    • Worked with a retail giant of South-East Asia in a demand planning project to optimize the inventory and stock outs by sales prediction.

    • Built a credit risk model to predict probability of loan applicants who will default on their loans using Logistic
    regression to facilitate client to make decisive actions based on the model, lowering the bad loans by 20%.

    • Transmuted actionable insights to stakeholders by creating dashboards in Tableau.

  • 2014 - 2016

    Data Analyst

    • Worked in a demand forecasting project for volume of hypermarkets for fast moving consumer goods at diverse levels of SKU and automate processes in R for all SKUs and optimizing the inventory management to reduce the
    wastage cost by 10% on average per SKU.