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Staff Data Scientist at Grid Dynamics | Data Science | Machine Learning | Deep Learning

About

Hello, and thank you for stopping by! With 8+ years of extensive experience in Data Science, I bring a demonstrated track record of delivering exceptional project outcomes. I am proficient in utilizing advanced statistical tools and techniques to analyze and interpret complex data sets, while consistently taking a leading role in driving end-to-end machine learning pipelines from solution design to deployment. My strong expertise includes developing forecasting models, optimization strategies, and personalization systems using a wide range of machine learning and deep learning algorithms. I possess a versatile skill set encompassing a wide range of core competencies and technical expertise. Allow me to highlight my expertise in the following key areas: Core Competencies: Data Science • Machine Learning • Time-Series Forecasting • Predictive Analysis • Statistical Modelling • Deep Learning • Natural Language Processing (NLP) • Google Cloud Platform • Data Visualization • Data Structures & Algorithms • Machine Learning Operations (MLOps) • Story boarding • Project Management • Agile Methodologies Technical Skills: Python • SQL • R Programming language • Google Big Query • Jupyter Lab • Pyspark • Apache Airflow • Cloud Computing • Git • Tableau • PowerBI If you bring me data, I don't just work, I deliver great results! As a Data Scientist, I am deeply passionate about leveraging data to solve real-world industry problems and drive business outcomes for organizational success. My commitment to staying on the cutting edge of emerging trends and technologies in the field enables me to leverage the latest tools and methodologies to deliver optimal results. I thrive in collaborative, cross-functional environments and possess the ability to communicate complex data insights to a wide range of stakeholders.

Location

Bengaluru

AI stack

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Work experience

Staff Data Scientist at Grid Dynamics, Sep 2024 - Present · 2 yrs 1 mo, ▸ Transformed a market-specific demand forecasting solution into a globally scalable platform by modularizing reusable forecasting logic into a versioned Python library and externalizing market-specific behavior through configuration, reducing new-market onboarding from 3 months to 2 weeks while scaling deployments from 1 to 20+ global markets. ▸ Standardized feature engineering across global markets by consolidating fragmented implementations into a reusable framework, delivering consistent model-ready datasets and eliminating duplicate engineering effort. ▸ Strengthened platform reliability by implementing automated data validation and quality controls across enterprise forecasting pipelines, improving data consistency and reducing production data issues. ▸ Partnered with regional business and planning teams across multiple geographies to translate market-specific forecasting requirements into configuration-driven workflows, accelerating global rollout while eliminating per-market code forks. ▸ Productionized the forecasting platform on Azure Databricks with Azure Data Factory orchestration, establishing version-controlled deployments, standardized release management, and engineering best practices to operate a single scalable codebase across all production markets.

Lead Data Scientist at Grid Dynamics, Aug 2023 - Aug 2024 · 1 yr 1 mo, ▸ Led a team of data scientists to deliver PricingAI, an enterprise Revenue Growth Management platform supporting a business with $6.5B in net revenue, owning solution architecture, ML model design, and production deployment. ▸ Replaced a third-party pricing solution with an in-house Bayesian hierarchical modeling framework in Stan, estimating own- and cross-price elasticities across complex product portfolios. ▸ Built a price optimization engine that translated elasticity estimates into constrained pricing recommendations optimized for revenue, profit, and volume objectives. ▸ Deployed the platform into production, enabling global finance teams to simulate portfolio-wide pricing scenarios and evaluate business trade-offs before execution. ▸ Scaled the solution across 8+ global markets, establishing PricingAI as the enterprise pricing decision platform for finance and commercial teams.

Apprentice Leader/Lead Data Scientist at Mu Sigma Inc., Jan 2022 - Aug 2023 · 1 yr 8 mos, Leading a team, fostering client relationships, and developing innovative strategies to achieve business objectives ▸ Conducted Customer Segmentation by creating customer profiles based on demographic and sales behavior using k-means algorithm and heuristics. The resulting segmentation strategy has yielded an increase in Incremental Revenue and Conversion Rate. ▸ To ensure effective decision-making when it comes to audience segment selection, I have developed a comprehensive A/B testing framework. This framework provides clear comparisons of different audience segments, allowing us to make data-driven decisions that maximize overall results. ▸ Further, I have designed and implemented a personalized product recommendation system that utilizes advanced deep learning algorithms such as LSTM. By predicting the next best product based on customer sales behavior and purchase patterns.

Senior Data Scientist at Mu Sigma Inc., Nov 2020 - Dec 2021 · 1 yr 2 mos, Bengaluru, Karnataka, India, ▸ As the key team member, I successfully optimized price and promotion strategies by leveraging machine learning models. This approach resulted in a significant 8% increase in company revenue. ▸ To start with, I first conducted a thorough analysis of historical sales data to identify the key factors affecting product demand forecasting. Based on these insights, I developed robust forecasting models that achieved an impressive 86% accuracy, enabling us to make data-driven price and promotion decisions. ▸ To further enhance our forecasting capabilities, I designed and developed the New Product Launch Simulator Tool. This tool utilized advanced machine learning algorithms to accurately forecast demand for new products across diverse markets. 4 new products were successfully launched in the market using this tool which clearly demonstrates the effectiveness of this capability. ▸ Additionally, I conducted a detailed analysis of product cannibalization effects, providing valuable insights into product development and profitability.

Data Scientist at Mu Sigma Inc., Nov 2019 - Oct 2020 · 1 yr, ▸ Implemented robust time-series forecasting models which accurately predicted natural gas prices with a window of 82% to 91% accuracy for the next seven-day forecast. This enabled stakeholders to make informed decisions and increase profitability for the company. ▸ Built a classification model that predicted the occurrence of specific events. This model provided valuable insights into the likelihood of various outcomes, helping clients make informed business decisions. ▸ To streamline the data collection and analysis process, I leveraged my Python skills to scrape data from various platforms and automate the daily flow of data from third-party platforms to Google Big Query. ▸ Further, I designed and developed a user-friendly dashboard in Power BI that visualized forecasted Natural Gas prices and various other Key Performance Indicators for traders and stakeholders.

Data Scientist at Tata Consultancy Services, Jun 2018 - Nov 2019 · 1 yr 6 mos, Bengaluru, Karnataka, India, ▸ As part of my responsibilities, I conducted an extensive analysis of Kylin and Druid data stores. After careful consideration, selected Apache Druid for its superior analytical capabilities and fast query performance to power Tableau for efficient slice-and-dice analytics on large datasets, resulting in a 10x faster query time. This, in turn, enabled end-users to make faster decisions and increase productivity. ▸ To further streamline the data ingestion and processing pipelines, I designed end-to-end automated pipelines using a range of Hadoop tools and frameworks. This approach allowed for a more efficient and reliable data processing workflow, enhancing the quality and speed of data analysis. ▸ In addition, I developed a machine learning model to effectively target potential customers for digital coupons, which led to a significant increase in overall digital adoption. By leveraging the propensity model, we achieved a 40% increase in the redemption rate, while also boosting digital engagement for these users.

Education

Bachelor of Technology - BTech, Visvesvaraya Technological University