•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