Jenarthanan Rajenthirana

• Delivered advanced analytics and AI solutions supporting Private Equity clients across multiple industries, enabling data-driven commercial due diligence, valuation assessment, churn prediction, and revenue optimization initiatives.
• Led the end-to-end development of Athena, an enterprise LLM-powered knowledge and insight platform, enhancing investment research efficiency and internal consulting workflows.
• Designed and deployed scalable AI architectures on Azure (Azure OpenAI, Azure ML, Cognitive Search), achieving 40% organizational adoption within 6 months.
• Processed and structured 10+ years of client engagement data and delivery documents to enable machine learning ingestion, semantic search, and downstream analytics for investment analysis.
• Developed and maintained SQL and ETL pipelines aligned with strong data governance standards to support high-quality analytics for Private Equity engagements.
• Built AI-powered semantic search and chatbot systems using embeddings and RAG pipelines, collaborating with consulting teams to translate business requirements into tools supporting Private Equity decision-making.

• Identified client business domains and audited target company’s financial statements for VDR audit and pipeline.
• Performed web scraping on publicly available sources, collected necessary data from Alteryx databases, and integrated data into client databases to enhance quality and coverage.
• Conducted revenue and customer churn analysis, developing statistical models based on end markets and customer segmentation.
• Generated actionable insights on business growth and provided recommendations to empower private equity firms in decision-making.

• Led e-Commerce projects for major online retailers, involving web scraping, data extraction, and scheduling with AWS Airflow.
• Transformed and cleansed data using SQL queries to enable robust business insight analysis.
• Analyzed third-party big data with PySpark and implemented a device mapping system to identify WiFi terminal devices’ networks.
• Expanded the device mapping project across 9 countries following initial success.

Collaborated with a global team of 50 changemakers to capture diverse perspectives and emotions of youth from diverse geographic regions.

• Collected social media data from Facebook, Reddit, and Twitter to analyze sentiment of young people.
• Performed topic modeling, sentiment analysis, and produced data visualizations to communicate insights.

• Designed BIO tag sets and curated annotated data from official government documents.
• Developed a Tamil name density recogniser by applying deep learning and distributed semantic techniques.
• Developed a scalable, semi-automatic approach to create annotated corpora for regional languages.