Senior Data Scientist AI, MLOps, Azure Expert

SADASHIV GUPTA

Senior Data Scientist

Phone: +91-971******* Email ID: *************@*******.*** Availability: Immediate

Summary

Senior Data Scientist with 7+ years of experience applying machine learning, generative AI, and advanced analytics to solve complex business problems across enterprise products and consulting engagements.

Strong background in Generative AI and LLM-based systems, including RAG architectures, agentic workflows, and natural language interfaces for business applications.

Proven ability to translate ambiguous business problems into scalable data science solutions that drive efficiency, adoption, and revenue impact.

Extensive experience working with large, complex datasets across commerce, marketing, retail, and customer behavior domains.

Extensive experience delivering scalable machine learning solutions using Python, XGBoost, regression trees, clustering, and time-series methodologies.

Demonstrated success in productionizing machine learning models for forecasting, clustering, and prediction in enterprise environments.

Strong understanding of AI evaluation, experimentation, and KPI definition, ensuring measurable and explainable outcomes.

Hands-on experience with Azure data and AI ecosystem, including Azure OpenAI, Synapse Analytics, and Power BI.

Recognized for driving high adoption of AI features through user-centric design, interpretability, and business alignment.

Experienced in stakeholder engagement and consulting, effectively communicating insights to technical and non-technical audiences.

Background in marketing analytics, pricing, and customer intelligence, supporting data-driven decision making for Fortune 500 clients.

Adept at working in fast-paced, cross-functional teams, collaborating with product managers, engineers, and leadership.

Track record of improving operational efficiency and reducing manual effort through automation and intelligent systems.

Passionate about building reliable, scalable, and responsible AI solutions that deliver sustained business value.

Technical Skills

Programming & Query Languages: Python, SQL, Advanced Excel

Machine Learning & Statistics: Supervised Learning, Unsupervised Learning, Regression, Logistic Regression, Decision Trees, XGBoost, Clustering (K-Means), Time-Series Forecasting, Predictive Modeling, Propensity Modeling

Generative AI & LLMs: Large Language Models (LLMs), Azure OpenAI, OpenAI APIs, Retrieval-Augmented Generation (RAG), LangChain, LlamaIndex, Prompt Engineering, Agentic AI

Natural Language Processing: NLP, Text Similarity, Sentiment Analysis, Topic Modeling, Document Classification, Text Summarization, OCR (Tesseract)

Data Engineering & Analytics: ETL Pipelines, Feature Engineering, Azure Synapse Analytics, Telemetry Analysis, KPI Definition, Data Transformation

Visualization & BI: Power BI, Interactive Dashboards, Executive Reporting, Self-Service Analytics

Cloud & Platforms: Microsoft Azure, Azure OpenAI Services

Web & Deployment: REST APIs, Flask, Model Deployment, API Integration, UI Automation

Libraries & Tools: Pandas, NumPy, Scikit-learn, PyCaret, Selenium

Experience

Data Scientist (IC3) Microsoft - Enterprise Commerce 06/2022 – Present

Drove the modernization of enterprise commerce workflows by contributing to a unified AI-driven experience replacing legacy CRM systems.

Led the application of agentic AI and LLM orchestration to automate quote-related workflows using natural language commands.

Applied LlamaIndex-based orchestration to enable seamless interaction between AI agents, APIs, and UI-level operations.

Accelerated seller productivity by enabling natural language–driven quote creation and modification through AI automation.

Delivered a LLM-powered data analysis agent enabling chat-based deal comparison, insights, and decision support for Microsoft sellers.

Applied Azure OpenAI and OpenAI models to translate natural language queries into data transformations and visual analytics.

Contributed to high user adoption by delivering one of the top 3 most-used AI assistant features within the commerce platform.

Advanced proposal intelligence capabilities by implementing a RAG-based summarization system using Azure OpenAI, SQL, and LangChain.

Improved AI assistant discoverability and engagement by ~4.5 through automated proposal insight generation.

Applied unsupervised learning techniques to cluster 200K+ SKUs into 600+ product categories using text similarity and K-means clustering.

Developed machine learning–based quantity forecasting models leveraging XGBoost, regression trees, and customer behavioral data.

Improved renewal quote accuracy and structure by integrating demographic, purchase, and product-level signals into prediction models.

Led AI feature performance evaluation through large-scale telemetry analysis to assess usage, effectiveness, and business impact.

Designed and implemented Azure Synapse Analytics pipelines to enable scalable data processing and reporting.

Established a self-serve Power BI dashboard as the single source of truth for leadership-facing product performance metrics.

Defined and operationalized KPIs across 10+ deal stages to track deal velocity, identify bottlenecks, and benchmark pilot outcomes.

Data Scientist (IC2) Microsoft Microsoft Edge Product 07/2021 - 06/2022

Supported growth initiatives for the Microsoft Edge product by applying data science and analytics to large-scale user migration efforts.

Applied Python, SQL, and Excel-based analytics to evaluate and optimize user retention campaigns.

Improved browser migration outcomes by driving a 6 increase in campaign performance through data-driven optimization.

Analyzed user behavior and engagement patterns to identify levers impacting retention across key markets.

Translated analytical findings into actionable recommendations for product and marketing stakeholders.

Designed and delivered interactive Power BI dashboards to track adoption, engagement, and campaign effectiveness.

Enabled data-driven decision-making by standardizing global user adoption metrics across regions.

Partnered with cross-functional teams to align analytics outputs with product and growth goals.

Strengthened experimentation and measurement practices through consistent tracking and performance reporting.

Data Science Consultant Boston Consulting Group 03/2020 - 06/2021

Led survey design, response validation, and statistical modeling for a consumer behavior study for a Fortune 10 tech client.

Applied logistic regression and statistical analysis to assess purchase likelihood and touchpoint preferences.

Analyzed data from 14K+ respondents across 12 product categories to extract actionable insights for marketing strategy.

Guided the reallocation of advertising investments across online and offline channels, improving marketing ROI.

Built and deployed an OCR-based document classification system using Python, Tesseract, and Flask.

Automated document ingestion, parsing, and categorization for HR processes, reducing manual effort by >77%.

Served as onsite data science consultant on multiple fast-paced projects for Fortune 500 clients.

Delivered forecasting models to support demand planning and business analytics initiatives.

Developed pricing models to optimize client revenue and competitive positioning.

Applied propensity modeling to enhance client targeting and conversion strategies.

Designed and implemented end-to-end data pipelines for survey and operational data processing.

Translated complex analytical findings into clear, actionable recommendations for non-technical stakeholders.

Collaborated with cross-functional teams to integrate data science insights into business strategy.

Demonstrated versatility by contributing to multiple client engagements simultaneously in high-pressure consulting environments.

Data Scientist The Smart Cube (Now WNS) 06/2018 - 02/2020

Developed market mix models using multiple regression for a global beverage brand to quantify the impact of pricing, distribution, and media spend.

Generated actionable insights on growth drivers, sales elasticity, and ROI, informing the client’s Annual Operating Plan.

Scraped and analyzed 100K+ Amazon product reviews for a U.S. hardware tools company using Python, Selenium, and NLP techniques.

Applied sentiment analysis, theme extraction, and gender detection to identify customer pain points and guide product and marketing improvements.

Automated spend classification workflows for a U.S. automotive client using text similarity algorithms, improving precision and reducing manual effort.

Executed multiple data science and analytics projects across retail and financial clients, leveraging predictive modeling, regression, clustering, and time-series forecasting.

Built scalable analytical solutions that reduced turnaround time and enhanced business decision-making for clients.

Supported the setup of an internal Machine Learning Center of Excellence, standardizing model development pipelines and reusable components.

Applied Python and advanced analytics libraries to deliver actionable insights and improve client operational efficiency.

Translated complex analytical outputs into executive-ready recommendations for client leadership teams.

Collaborated with cross-functional client teams to align analytics solutions with business objectives.

Improved accuracy and reliability of predictive models by implementing best practices in data cleaning, feature engineering, and model validation.

Leveraged clustering and segmentation techniques to identify patterns and customer insights across large datasets.

Delivered measurable business impact by combining statistical modeling, data engineering, and visualization to support strategic planning.

EDUCATION

Master of Science in Applied Operations Research

Faculty of Mathematical Sciences, Delhi University 2016 – 2018

Bachelor of Science (Honors) in Statistics

Hindu College, Delhi University 2012 – 2015

Contact this candidate