Headline

Decision Scientist | Data Scientist | LLMs, NLP, RAG, Deep Learning | Retrieval Systems | Predictive Modeling | MLOps | Tesco | Ex-IBM

About

🚀 Data Scientist | Marketing Analytics @ IBM – Client Science & Insights (CSI) Team As part of IBM’s CSI team, I develop and deploy data science models that power IBM's marketing intelligence engine. My work helps drive product sales, cross-sell, and upsell by turning engagement data into actionable strategies. 🔍 What I do: Design and deploy cloud-based machine learning models that run continuously on distributed clusters to deliver real-time insights. Segment companies based on their engagement with IBM websites, events, and digital media. Recommend high-impact job roles and accounts for marketing teams to target, improving lead-to-opportunity conversion. Drive data-driven marketing tactics by uncovering behavioral patterns and predicting customer intent. Collaborate across marketing, data, and product teams to personalize outreach and maximize campaign effectiveness. 💡 I’m passionate about blending data science with real-world business outcomes — delivering scalable solutions that support IBM’s go-to-market strategies and transform how we connect with clients. Applied Scientist with 4+ years of experience building and deploying large-scale AI systems across LLMs, NLP, Deep Learning, and Predictive Modeling. I specialize in designing retrieval pipelines, scalable ML architectures, recommendation systems, and automation using GenAI and RAG frameworks. I have end-to-end ownership experience — from data engineering and experimentation to MLOps, deployment, and collaboration with GTM and engineering teams. My work has directly improved revenue, conversion rates, customer insights, and operational efficiency at global scale. Core strengths: Building semantic search & embedding-based retrieval systems Fine-tuning LLMs, RAG pipelines, prompt engineering Deep learning models for classification & computer vision Designing PySpark + cloud ML pipelines for production MLOps with Docker, CI/CD, low-latency APIs Strong statistical modeling, experimentation (A/B), and explainability (SHAP) I’m passionate about solving complex business problems using clean architecture, scalable ML design, and modern GenAI techniques. Open to AI/ML Engineer, Applied Scientist, and Data Scientist roles where I can build impactful, production-ready machine learning systems.

Location

Bangalore Urban district

AI stack

claude code

claude

github copilot

hugging face

langchain

langgraph

Work experience

Senior Decision Scientist at Tesco, May 2026 - Present · 5 mos, Hybrid

Data Scientist at IBM, May 2025 - Apr 2026 · 1 yr, Bengaluru, Karnataka, India, Data Scientist - CHQ, Marketing at IBM As a Data Scientist at IBM, I leverage advanced statistical modeling, machine learning, and data science techniques to solve complex business problems. My focus is on transforming raw data into actionable insights that drive business decisions and provide a competitive edge. Key responsibilities include: Formulating data-driven hypotheses, building models, and testing conclusions to guide strategic decisions. Extracting, transforming, and combining diverse datasets to uncover hidden insights. Developing and deploying statistical and machine learning models to optimize business outcomes. Providing actionable recommendations based on data analysis to influence key business strategies. Effectively communicating insights and recommendations to leadership for informed decision-making. Built semantic similarity & embedding-based retrieval for lead enrichment, improving precision by 22% across 40K+ accounts/week. Prototyped a GenAI insights agent using LLMs + prompt chaining → reduced manual operations by 55% for 1K+ tasks/month. Developed deep-learning propensity models with SHAP explainability, improving conversion lift by 18% over existing logic. Engineered PySpark scoring pipelines across 7 global markets, reducing runtime from 4 hours → 90 minutes with automated drift monitoring. Partnered with GTM & Data Engineering for scalable deployment and robust data-quality workflows.

Data Analyst at IBM, Oct 2021 - Apr 2025 · 3 yrs 7 mos, India, Backup Team Lead, Client - Dun & Bradstreet | IBM Consulting Architected data marts with 500M+ records, enabling low-latency ML inference → 37% faster analytics cycles. Built supervised ML models for churn mitigation and account expansion, unlocking 15% uplift in retention opportunities. Led feature engineering → validation → deployment lifecycle, delivering 30% faster time-to-production. Mentored a team of 4 data scientists on scalable ML workflows, debugging, and deployment best practices.

Intern at Cognizant, Mar 2021 - Jul 2021 · 5 mos, Bengaluru, Karnataka, India

Education

Bachelor of Engineering - BE, Information Technology, Dayananda Sagar Academy Of Technology And Management, Bangalore