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A Kaggle Silver Medalist Data Scientist with Expertise in Energy Analytics and Machine Learning


The data scientist professional is a Kaggle Silver Medalist with more than 5 years of experience in providing analytical solutions in the energy sector. They utilize Python, R, SQL, and JMP to uncover usage patterns, predict equipment failures, and optimize machine performance for their clients. Their expertise includes statistical modeling, survival analysis, regression analysis, Bayesian models, predictive analytics, data mining, data wrangling, data visualization, data scraping, and neural networks. They have a deep understanding of MS SQL and AWS Redshift databases and are proficient in machine learning technologies. 

Currently, they work as a Senior Engineer-Data Analytics at Bloom Energy in Mumbai, where they analyze large and diverse datasets using big data tools and machine learning algorithms. They also possess knowledge in operations research, statistical analysis, and reliability and life modeling. Overall, they possess excellent technical skills, strong problem-solving abilities, and effective communication and interpersonal skills to tackle complex challenges.

Work Experience


Senior Engineer-data Analytics
  • Assisting in analyzing diverse sets of data using Big Data tools and machine learning to find common patterns, themes, and trends.
  • Understanding statistical learning, scheduling/strategy optimization, and stochastic modeling for operations research and statistical analysis.
  • Compiling data for failure analysis and building life estimation models in reliability and life modeling.
  • Successfully implemented a regression model to predict peak core temperature for the entire fleet, resulting in improved machine efficiency.
  • Developed a survival model (Hotbox) to predict failure probabilities based on multiple factors, leading to increased reliability through early interventions.
  • Identified a key early indicator of machine failure rate using a Bayesian graph model.
  • Created a classification algorithm using logistic regression and customized logic to identify machines for performance improvement.
  • Built a classification algorithm with customized logic to identify machines in need of replacement.
  • Developed a tool utilizing piecewise linear regression and the Douglas peucker algorithm to calculate non-linear resistance and gradient for SOFC.
  • Created a script for implementing different containment strategies to prevent early module failures in machines.


Assistant Manager
  • Procure energy efficiently in volatile markets.
  • Use predictive modeling to plan for unexpected situations and uncover hidden opportunities.
  • Develop sophisticated models for predicting consumer monthly bills.
  • Analyze large quantities of meter data to improve operational efficiency, enhance customer experience, make informed decisions, and detect outages.
  • Process data in real-time while also storing and analyzing historical data.
  • Cluster customers based on their daily energy usage to optimize load balancing and identify those who can benefit from energy reduction programs.
  • Perform meter readings, time series analysis, replacement value procedures, and data management tasks.
  • Developed an algorithm using R, LSTM(RNN), and ensemble algorithms (RandomForest, XGboost) for forecasting energy prices on an Energy Exchange.
  • Created a tool using R, Glmnet, and multivariate time series analysis for predicting consumer bills one month ahead.
  • Conducted churn analysis using R and Random Forest to identify consumers who are more likely to switch to other utilities.

Top POWER GENERATION COMPANY, Mumbai (2013 – 2018)

Executive – Power System Operations
  • Provide management reporting to guide daily actions and shape the overall business intelligence strategy.
  • Deliver actionable insights that provide real value to employees, customers, and partners.
  • Support back-office processes to facilitate efficient energy management.
  • Import energy-balancing data specific to settlement units.
  • Consolidate data from multiple sources using data preparation solutions like R, VBA, and Excel.
  • Created useful measures based on existing data.
  • Developed a tool using R and logistic regression for locating faults in equipment after failure.
  • Implemented automated daily report generation.
  • Designed a tool using VBA and Excel for automated daily outage scheduling.
  • Developed a tool for automated defect management.
  • Created a tool for demand forecasting using R, decision trees, pattern recognition, and multivariate time-series analysis.


  • Bachelor’s degree in Electrical and Electronics Engineering from National Institute of Technology, Tiruchirappalli in 2013
  • Achieved a CGPA of 7.41 during the course of study


  • Awarded a Silver Medal in the 2019 Kaggle competition for Microsoft Malware Prediction. 
  • Achieved a Silver Medal in the 2019 Kaggle competition for Santander Customer Transaction Prediction.
  • Currently ranked in the top 3% among Kaggle competitors, placing in the expert category in 2021.
  • Served as a Core Member of the Tata Power Innovation Council in 2018, focusing on identifying AI/ML applications in the utility sector.

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