United States
I architect end-to-end AI systems that generate $1M+ annual revenue impact per client. My expertise spans LLM fine-tuning, statistical modeling, and MLOps pipelines that transform raw data into actionable business intelligence. Current Focus: REV Multifamily Leasing Built the REV Leasing Score—an AI-powered analytics platform processing 300+ properties ($500M+ AUM). I deployed instruction-tuned Claude AI with advanced prompt engineering to extract 1,500+ data points from call transcripts, feeding proprietary statistical models that generate leasing effectiveness scores. The complete pipeline (VoIP integration → NLP processing → AWS RDS → React dashboards) reduced analysis time by 95% while delivering $1M+ revenue uplift per portfolio. Technical Stack: ML/AI: Python, TensorFlow, Scikit-learn, LangChain, LangGraph, Fine-tuning, LoRA Data Engineering: AWS (RDS, Lambda, EC2, SageMaker), PostgreSQL, ETL pipelines Analytics: Statistical modeling, NLP, sentiment analysis, predictive algorithms Visualization: React, Tableau, Power BI with real-time API integrations Previous Impact: At Whitehat Junior, I built sentiment analysis models for sales call evaluation, resulting in 40% higher net revenue for supervised teams. At Vedanta Aluminum, I implemented predictive analytics systems that reduced downtime by 15% and improved process efficiency by 10%. Certifications: Generative AI with Large Language Models (DeepLearning.AI & AWS) Supervised Machine Learning (DeepLearning.AI & Stanford) I thrive in hyper-growth environments where data science meets business strategy. Whether it's fine-tuning LLMs for domain-specific extraction, building real-time ML pipelines, or deploying scalable AI infrastructure, I deliver solutions that directly impact the bottom line. Looking for opportunities in AI/ML engineering, data science, or LLM development where complex technical challenges meet measurable business outcomes.
Owned end to end data science strategy and execution across pricing, retention, forecasting, and experimentation, partnering with Product, Engineering, Sales, and Operations to translate business problems into measurable solutions. Built and deployed an end-to-end real-time AI agent pipeline that extracted 1,200+ structured signals from sales-call transcripts and delivered actionable insights via client-facing dashboards, driving $3M+ average annualized revenue uplift per client portfolio. Built a lease rent pricing model using historical leasing performance, market signals, and property attributes to optimize pricing decisions; increased annual revenue by 12%. Developed a churn / retention prediction model to identify at-risk properties and trigger targeted interventions; improved retention by 20% through better prioritization and proactive outreach. Designed and scaled KPI frameworks and dashboards (activation, engagement, conversion, quality) to guide roadmap decisions and investment trade-offs; improved client engagement by 25%. Led end-to-end A/B and holdout experiments (power analysis, hypothesis testing, CUPED variance reduction) on messaging, reviews, and follow-ups; increased leasing conversion by 18%. Built and productionized forecasting models to inform staffing and capacity planning, improving operational planning accuracy and resource allocation. Developed and automated data pipelines and data quality checks using Python-based workflows with Airflow and Spark on AWS (S3, RDS, Lambda, SageMaker), ensuring robust, reusable pipelines for repeatable model retraining and near-real-time monitoring. Streamlined analytics workflows and pipeline performance, reducing manual processing by 80% and improving stakeholder self-serve access to decision-ready metrics. Implemented Git-based version control and code reviews to improve code quality and standardization across Python ML pipelines.
Built and maintained funnel KPI and goal-setting framework across activation, engagement, conversion, and retention; delivered insights that influenced product and growth investments. Designed and analyzed A/B tests across outreach sequencing, pricing prompts, and onboarding experiences; drove a 40% revenue improvement over floor average through iterative experimentation. Developed automated SQL/Python reporting and experiment readouts (Tableau/Power BI), reducing manual reporting by 65% and improving visibility into performance drivers. Performed cohort and retention analysis to identify drop-off points and behavioral segments; partnered with stakeholders to convert findings into roadmap actions.