Neha Madhavan

• Enhanced overall team efficiency by streamlining processes and implementing innovative solutions.
• Reengineered existing workflows for optimal efficiency, yielding notable improvements in staff productivity levels.
• Evaluate system requirements to develop tailored solutions addressing unique organizational needs.
• Reduce project completion time and maintaining a continuous improvement cycle by effectively managing resources and prioritizing tasks
• Maintain an good understanding of the business processes and valued SME data whilst assessing tech solutions
  
  In this role, apart from low-code PowerPlatform solutions, I also have the creative freedom to review, research, and support deployment of AI solutions (Microsoft Co-Pilot, Microsoft Power Automate) that could be standardised across the company.

• Refining and optimizing internal business processes with low-code tech to produce client-centric, high-value solutions.
• Creating detailed test plans, defining user testing criteria and overseeing development, testing and development of business solutions.
• Mapping out existing company processes, identifying pain-points, and leading transition to newer digital technologies; managing user training and engagement.
• Managing Continuous Improvement on released apps, planning and prioritizing backlog.
• Proven experience in Stakeholder Management, User testing Management, managing company-wide digitalization and increasing visibility of newer technologies.

Technologies: Microsoft Power Platform, Azure DevOps, AAD, Visio, Sharepoint, and Confluence

Soft Skills: Cross-functional collaboration, project management, business analysis, functional support, technical documentation

• Supported team collaboration by providing clear communication of complex findings through visualizations and reports.
• Collaborated with cross-functional teams to identify opportunities for process improvement and increased efficiency.
• Improved internal knowledge sharing by developing comprehensive documentation, training materials outlining standard operating procedures for various tasks.
• Increased efficiency by streamlining data analysis processes and implementing automation tools.

• Feature Engineering: Image, video, and post-based feature recommender systems and estimation of feature impact based on content type.
• Custom Classifications: Building and training a custom classifier system for images based on associative rules - use of recognition systems to frame associative rules by testing image datasets - mapping of rules and creating feature recommender lambda.
• Automation: Data pulls and generic pipelines, generic pull script to pull data from neo4j, and translation of cypher queries into python.
• Research: Explainable AI, Content vector similarity, Misinformation detection models, and explainability models.