• Design an AI-powered data quality monitoring framework for Azure Data Lake within LexisNexis Risk Solutions’ Insurance Technology division

• Apply machine learning and time-series methods to detect anomalies, missing values, formatting issues, and other data quality problems

• Evaluate Azure-native AI/ML tools and third-party solutions for data quality monitoring

• Build dashboards to visualize data trends and quality signals

• Deliver a proof-of-concept monitoring system with clear design and operational recommendations

• Automate the Bridger Watchlist research process for LexisNexis Risk Solutions
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• Analyze assigned URLs to understand data formats and identify anomalies
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• Document findings and develop Python scripts for web scraping and data extraction

• Work with HTML, JSON, and XML data structures and apply regex-based extraction techniques
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• Build repeatable, automated workflows that improve operational efficiency

• Research and organize metadata from multiple internal sources to support a ChatFlow-based knowledge system at LexisNexis Risk Solutions
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• Interview data engineering teams to understand metadata needs and current workflows
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• Gather, analyze, and document metadata to identify key patterns and insights

• Contribute recommendations that improve metadata accessibility and internal processes

• Support the development of a unified, searchable internal knowledge base

• Optimize PowerBI data refresh processes by integrating Azure Data Factory (ADF) with the PowerBI REST API

• Build secure API authentication and improve pipeline reliability to reduce refresh failures

• Enhance error handling to address timing conflicts between ADF and Synapse updates

• Design scalable ADF pipelines and implement automated PowerBI refresh triggers

• Collaborate with EDI and PowerBI engineering teams to deliver a more stable, efficient reporting workflow

• Develop an AI-powered framework for real-time data quality monitoring in Azure Data Lake for LexisNexis Risk Solutions
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• Design and implement anomaly detection and time-series models to identify missing values, formatting issues, duplicates, and unusual data trends

• Analyze large-scale insurance datasets to detect and categorize data quality problems

• Evaluate Azure-native machine learning tools alongside third-party solutions

• Recommend the most effective approach for automated, scalable data quality management

• Design and deliver a performance-monitoring dashboard for AES using Power BI

• Gather workflow and performance data to identify key metrics and visualization needs

• Apply UI/UX dashboard design principles and create iterative mock-ups

• Develop and refine the dashboard based on client requirements and ongoing feedback

• Test the solution across devices and produce final documentation, a project report, and a client presentation

• Develop a digital customer success toolkit for Georgia-Pacific to automate Quarterly Business Reviews (QBRs)

• Design standardized QBR templates and build ROI calculation models

• Create dashboards that visualize operational efficiency, labor savings, and performance trends

• Integrate data from IoT dispenser systems to generate meaningful, data-driven insights

• Develop customer success playbooks and engagement strategies

• Deliver a scalable, automated framework that enhances customer engagement and demonstrates measurable business impact