Headline

Data Analytics

About

Data Analyst & AI Solutions Developer with 1.5+ years of experience at WaterLabs.ai, specializing in bridging the gap between raw data, predictive insights, and automated operations. I build production-grade data pipelines, predictive models, and Generative AI workflows that eliminate operational bottlenecks and deliver measurable ROI. Key Technical Impact & Outcomes: • Predictive Analytics & ML: Engineered a Random Forest model for predictive analysis, achieving an 85% boost in claim prediction accuracy and significantly reducing denial rates. • Workflow Automation & AI Tools: Designed automated data ingestion pipelines and Gen AI agent workflows (n8n, LLM integrations) to streamline daily operations. • Data Orchestration & Visualization: Built end-to-end Python & SQL data pipelines alongside interactive Power BI and Advanced Excel dashboards to drive cross-functional decision-making. Core Tech Stack: • Data Analytics & SQL/Python: Python, SQL, Advanced Excel, Data Pipeline Architecture • BI & Visualization: Power BI, Data Modeling, Executive Reporting • Machine Learning & Gen AI: Random Forest, Predictive Modeling, Generative AI, AI Tools (n8n, LLM APIs) Beyond technical execution, my experience in public speaking and team leadership allows me to effectively translate complex technical solutions into clear business outcomes for stakeholders. 📬 Let's Connect: Reach out via email at puneettiwari77711@gmail.com.

Location

Bengaluru

AI stack

claude code

claude

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Work experience

Data Analyst at Waterlabs AI, Oct 2024 - May 2026 · 1 yr 8 mos, Bengaluru, Karnataka, India, Machine Learning & Predictive Modeling: Designed and deployed a comprehensive Random Forest classification model to predict insurance claim outcomes (Approval/Denial). Engineered high-impact features from complex datasets—including claim history, ICD-10 diagnosis codes, and patient demographics—to maximize model sensitivity and precision. Achieved an 85% improvement in prediction accuracy compared to manual audits, significantly reducing claim processing latency. Leveraged Python (Pandas, NumPy) for data manipulation and Scikit-Learn for model training; performed deep-dive Exploratory Data Analysis (EDA) using Seaborn and Matplotlib. Delivered data-driven validation for the TPG client, effectively minimizing manual intervention and accelerating the end-to-end claim lifecycle. Intelligent Process Automation: Architected over 20 autonomous agents using Playwright, Selenium, and PyAutoGUI to streamline workflows across web-based and legacy desktop applications. Integrated advanced tooling, including Large Language Models (LLMs) and OCR, to automate document processing and enhance operational scalability.

Education

B.Tech, Artificial Intelligence & Machine learning, RNS Institute of Technology - India

Little Flower House Senior Secondary School