Lanre Uzamere

Designed and deployed over 5 production-grade LLM-based applications, including chatbots and content generation tools, which improved user engagement by approximately 35%. Led the end-to-end development of a conversational AI platform using fine-tuned LLMs integrated with a MERN stack, and implemented a medallion architecture-based Lakehouse on Microsoft Fabric. Built agentic AI models for automat

Led a cross-functional team to design, develop, and deploy machine learning models and analytics solutions that drove business growth and risk mitigation. Conducted data-driven analysis of user behavior and product performance, resulting in a 25% increase in user activation rates and an 18% improvement in customer lifetime value. Designed and implemented a robust data lake using GCP's BigQuery and

Improved system monitoring and reliability using Prometheus, Grafana, and AWS CloudWatch, ensuring SLA compliance for data platforms. Designed and deployed a scalable ETL pipeline using Apache Spark and AWS Glue, processing over 10TB of transactional data daily for real-time analytics. Developed a machine learning model using Python and TensorFlow to predict customer churn, achieving 85% accuracy

Developed real-time data pipelines using Apache Kafka, Spark Streaming, and AWS Kinesis, capable of processing 10 million events per second. Created a distributed ETL pipeline using Airflow and Apache Spark to process 500 GB of data daily, improving job execution times by 45%. Implemented a data lake architecture on AWS (S3, Glue, & Athena) to centralize data storage, reducing data retrieval laten

Spearheaded the design of a transformer-based NLP fraud detection system, reducing false positives by 25% and increasing detection accuracy by 40%. Built end-to-end machine learning pipelines using Python and TensorFlow for predictive analytics, improving forecasting accuracy by 35%. Developed an NLP-driven chatbot handling 50% of customer inquiries, cutting average response time and reducing supp

Led a team of 5 analysts to deploy Tableau Server solutions for Fortune 500 clients, achieving a 98% user satisfaction rate for real-time dashboards. Engineered Tableau dashboards for clients processing over 10TB datasets, reducing query runtime by 45% through Spark integration and data model optimization. Designed and delivered over 15 Tableau training programs for 150+ professionals, resulting i

Led social media analytics strategy, increasing engagement rates by 45% through audience insights and A/B testing. Conducted deep-dive analysis on user behavior, enhancing follower retention by 23% through strategic engagement strategies. Developed REST APIs using Python Django Rest Framework and MongoDB for robust data management.

Built Python/SQL pipelines to automate quarterly investor reports, cutting manual effort by 40 hours/month and improving stakeholder data accessibility. Performed Data Analysis of Asset & Liability analysis to determine respective values/worth of 5 different Oil Companies for investment decisions. Scraped and analyzed over 50 competitor product features using NLP (BERT embeddings) and clustering,

Partnered with product, sales, and marketing teams, contributing to a 20% boost in quarterly revenue through data-driven insights. Implemented real-time analytics dashboards, reducing decision-making time by 50%. Collaborated within a cross-functional analytics team, enhancing collaboration between marketing, product, and data teams.

Studied Mathematics at the University of Ibadan. Gained foundational knowledge in mathematical principles and problem-solving techniques.