As a part of Business Intelligence and Data Management Team, contributed to a Critical Data migration project transitioning from Legacy Store Line POS to NextGen Emerald POS system across 2000+ stores for top brands like Food Lion, Giant Food, Hannaford, and Stop & Shop. Maintained data models and data pipelines ensuring accurate and consistent flow of transaction data to new POS through NCR Cloud, Kafka, MuleSoft APIs, IBM MQ, and Azure Blob Storage. Built ETL processes to extract, transform, and load data from both POS systems, enabling seamless integration across Ahold Delhaize USA’s analytics and reporting tools, ensuring data quality. Developed Power BI dashboards, providing real-time insights into transaction metrics, customer trends, and data integrity across the organization. Automated the QA test execution process by leveraging Jenkins to trigger data flow to Azure Data Lake, where XML TLOG’s and JSON files of transactions were retrieved for validation. Developed Python scripts in Azure Databricks to validate data structure, detect discrepancies, mismatches, and initiate RAD process in JIRA. Worked closely with QA, finance, and technology teams across Ahold Delhaize USA to understand business requirements with the organization’s strategic goals. Collaborated closely with POS and Integration teams to monitor and validate data transmission, ensuring reliable data exchange between POS systems and downstream applications. Responsible for providing daily status on Design Reviews & Requirements to business customer / IBM US coordinators.Estimating for the project's schedule, effort, and cost using configuration and risk managementPerformed quality assurance reviews to guarantee adherence to industry standards and best practices

. Improved website traffic and lead generation through optimized marketing strategies with target audiences and aligned with brand messaging according to client requirements.• Developed SEO-driven automated content optimization using new & innovative AI tools and prompts for websites, blogposts and social media via keyword research and competitive analysis to enhance search engine rankings and increase organic traffic• Analyzed competitors to propose new marketing campaigns, like personalized services for members, discounted prices resulting in performance improvements, customer retention and loyalty.• Collaborated with clients to translate business needs into marketing and development strategies• Utilized statistical analysis using machine learning algorithms in Python and R to analyze market trends and customer behavior.• Conducted financial analysis and audits to drive revenue growth.• Created interactive dashboards using Tableau, making insights accessible to stakeholders.• Evaluated campaign performance metrics (KPI's) to assess effectiveness and drive future marketing initiatives.. Created 2D and 3D websites tailored to client requirements. Planned social media campaigns and created content calendars to boost engagement.

Real-Time Customer Behavioral Analysis: This Project mainly focuses on Enhancing customer satisfaction and engagement by leveraging analytics to monitor and improve interactions within Atos's PeopleSoft CRM/HRMS system and to track, analyze, and improve real-time customer interactions, service delivery, and overall customer experience.•Developed and executed complex SQL queries for data extraction and aggregation from the PeopleSoft CRM system, ensuring accurate and real-time data flow.•Cleaned and organized data sets for accurate and reliable data analysis using MS Excel (VLOOKUP, Pivot Tables, Charts/Graphs) . Built statistical analysis and predictive models using machine learning algorithms including ‘caret’, ‘Random Forest’, ‘linear regression’ and ‘decision trees’ using R to predict customer’s feedback score . Optimized data preparation using SAS to streamline reporting and ensure data consistency across CRM systems . Developed data models in Power BI to structure data, through table relationships and utilized Power Query Editor to clean, transform, and prepare data for analysis. . Developed interactive and visually appealing dashboards and reports in Power BI using DAX measures and published them with stakeholders to create compelling data stories, increasing customer retention by 25%. •Experience in PeopleSoft Finance major setups like business units, customer tax codes, and VAT location accounts. Also had knowledge of application designer and process schedulers on Windows platforms.•Utilized PS Query and MS SQL Server for creating and updating database queries •Documented database schemas and CRM integrations•Trained 20 customer team members and acted as the primary point of contact for end-to-end revenue and billing support, reducing resolution time by 33%.

Optimizing Patient Care through Predictive Analysis collaborated with symbiosis university of health and research, the main objective is to reduce patient readmission rates, to provide real-time patient care metrics and treatment effectiveness.Contribution: . Organized datasets using advanced MS Excel functions to ensure accurate data analysis and insights generation . Utilized SQL to extract essential data from Electronic Health Records (EHR) systems.. Handled large volumes of patient data, including demographics, medical histories, treatment records, and outcomes.. Resolved data inconsistencies and handled missing values to ensure data reliability.. Merged data from various sources to provide a unified view of patient data.. Performing statistical analysis to explore patient data trends and treatment effectiveness using libraries like pandas, ‘seaborn’ and ‘matplotlib’, also developed predictive models to forecast patient readmission risks using libraries such as ‘scikit-learn'. . Designed various visualizations in Power-BI, including bar charts, line graphs, and heat maps, to represent treatment outcomes, visit trends, and patient risks. . Coordinated with multidisciplinary teams, including clinicians and data scientists, to align analytical approaches with healthcare objectives. Achievements:. Achieved a 15% reduction in readmission rates through the implementation of predictive models.. Enabled data-driven decisions for healthcare providers, improving resource allocation and operational efficiency.