AI Data Scientist with 7+ years of experience designing, building, and deploying scalable AI/ML solutions. Profi-cient in developing traditional ML/DL models, NLP systems, and LLM-based workflows using LangChain, Lang- Graph and RAG. Skilled in Python, Flask, data analytics, Azure Open AI services. Proven success in creating scalable ML and AI pipelines and production-grade solutions that impact Millions of users.
Data Scientist
Bengaluru, Karnataka, India
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Summary
Designed and Deployed advanced Generative AI and ML solutions, significantly improving sales efficiency and product engagement for global users.
Highlights
Designed and deployed Generative AI workflows using Azure OpenAI with sales playbooks and CRM data, reducing proposal turnaround time by 60% and driving a 15% increase in deal conversion rates.
Built an AI-Driven Deal Desk Consultation Optimization tool, integrating LLM, Airflow, and Trino SQL to reduce manual analysis time by 80% and accelerate deal consultation efficiency across global teams.
Delivered personalized GenAI-powered email recommendations through cluster-based behavioral segmentation, achieving a 28% uplift in product usage and up to 95% feature adoption in pilot campaigns.
Partnered with cross-functional teams to coordinate deployments to 250K+ users across SMB, Mid-Market, and Staffing, while designing experimental frameworks and KPIs to measure adoption, product usage, and churn impact.
Developed and productionized a sentiment analysis pipeline leveraging RoBERTa models, improving classification accuracy by 25% and automating monthly reporting, reducing manual effort by 40%.
Data Scientist
Bengaluru, Karnataka, India
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Summary
Developed and deployed robust ML models for demand forecasting, customer segmentation, and anomaly detection, significantly enhancing marketing strategies and operational efficiency.
Highlights
Built ML models for wallet share prediction, improving customer profiling and enabling targeted marketing strategies to drive revenue growth.
Delivered 85% accurate demand & P&L forecasts, effectively supporting inventory optimization and revenue planning for key clients.
Analyzed promotions & loyalty programs for Costa Coffee, driving critical ROI insights, achieving churn reduction, and optimizing pricing strategies.
Applied advanced segmentation, CLTV, and price elasticity modeling techniques, boosting promotional effectiveness and increasing customer engagement across campaigns.
Developed an anomaly detection package (Z-Score, IForest, HBOS) in Azure Databricks, empowering store managers to identify irregular sales patterns and make data-driven decisions across regions and categories.
Associate Consultant
Chennai, Tamil Nadu, India
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Summary
Contributed to the development and implementation of ML models for churn prediction, forecasting, and data pipelines, enhancing customer retention and operational planning.
Highlights
Developed ML models for churn and risk prediction, enabling proactive customer retention strategies and reducing potential revenue loss.
Built a transaction volume forecasting model using FbProphet, improving capacity planning and optimizing TPF resource allocation for authorization requests.
Implemented robust data preprocessing and feature engineering pipelines to enhance model accuracy and reliability across various projects.
Partnered with client stakeholders to align forecasting insights with operational efficiency and risk management goals, ensuring strategic business impact.
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M.Tech
AI & ML
Grade: 8.2/10 CGPA
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B.Tech
Computer Science & Engineering
Grade: 74%
Python, SQL, Trino, Pyspark.
Pandas, Numpy, Matplotlib, Tensorflow, Keras, Scikit-learn, mlflow, Airflow, MLops Practices, CI/CD.
Supervised Models, Unsupervised Models, Market Basket Analysis, Time Series Forecasting.
NLTK, Topic Modeling, Sentiment Classification.
Transformers, ANN, RNN.
Prompt Engineering, RAG, Fine-tuning, Autogen, LLMOPS.
Inferential & Descriptive Statistics, Hypothesis Testing, A/B Testing, T-test, Chi-square, ANOVA.
Azure OpenAI, Data Bricks, Azure ML, Jira, Confluence, Vscode, Git, Github.
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Summary
Leveraged Generative AI and data to auto-generate first-draft sales proposals, reducing turnaround time, improving consistency, and increasing win rates by enabling sales teams to focus on personalization and strategy.
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Summary
Utilized AI and behavioral data to deliver personalized email recommendations, driving product adoption, engagement, and reducing account churn.
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Summary
Developed a sentiment scoring system from sales reps case comments to help leadership track perceptions, identify pain points, and improve Deal Desk effectiveness for faster deal support.
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Summary
Developed a forecasting tool to support strategic decisions in inventory, revenue planning, and resource allocation by offering insights into future demand and profit trends.
