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Vaishnavi M R

@vaishnavimr

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Data Engineer at TCS who cut retail data-processing runtime by 90% and migrated workloads to Azure Databricks.

India

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At TCS, I build Python and PySpark ETL workflows for retail pricing and markdown optimisation on Optumera. I reduced data-processing runtime by 90% through parallel execution and removing redundant computations.

I also led the migration of data-processing workloads to Azure Databricks, adapting existing code, resolving execution issues, and validating outputs.

At NLC India Ltd., I built solar radiation forecasting models for the Neyveli solar power plant, reaching an R² of 0.98. I used sliding-window techniques on weather datasets and benchmarked ensemble and reinforcement learning approaches.

I built Tartarus, a GitOps-oriented configuration management API with versioned, auditable infrastructure state, and am building PitwallAI, an F1 race strategy simulator that recommends pit windows using tyre-degradation modelling and Monte Carlo simulation.

Experience

Work history, roles, and key accomplishments

Current

Software Engineer - Data Engineering

Current

TCS

Mar 2025 - Present (1 year 8 months)

Build Python/PySpark ETL workflows for retail pricing and markdown optimisation on Optumera, processing 15 datasets across 20 pipeline components. Reduced data-processing runtime by 90% through parallel execution and led migration of workloads to Azure Databricks.

Python PySpark ETL Azure Databricks Apache Spark Threading data transformation Machine Learning

ML & Data Engineer, Intern

NLC India Ltd.

Jun 2023 - Jul 2023 (1 month)

Built forecasting models for solar radiation at the Neyveli solar power plant using Python, scikit-learn, TensorFlow, and XGBoost, reaching R² of 0.98. Applied sliding-window techniques to large-scale weather datasets and benchmarked ensemble and reinforcement learning approaches.

Python scikit learn TensorFlow XGBoost Forecasting Sliding Window Ensemble Learning Reinforcement Learning

ML Research Intern

IIT Kharagpur

Sep 2022 - Oct 2022 (1 month)

Analysed relationships among NSE Financial Services stocks using linear regression to identify and quantify stock-to-stock connections. Visualised stock relationship networks using Gephi and the Fruchterman–Reingold force-directed layout algorithm.

Linear Regression Data Analysis Gephi Data Visualization Fruchterman Reingold Algorithm