Eddala Bhuvanesh

Project Leadership & Lifecycle Management: Led end-to-end client implementations of HighRadius Cloud products , applying project management best practices, including risk assessment and mitigation, to ensure timely and successful project completion. I managed projects through all phases of the lifecycle, from requirements to go-live and hyper-care. Customer Adoption & Satisfaction: Evaluated and improved product adoption rates by 16% by establishing standard practices that enhanced overall customer stickiness. Through holistic ownership of the customer lifecycle, I drove a 19% increase in customer satisfaction by providing hands-on training and validating value outcomes. Data-Driven Product Enhancement & Prediction: Developed a Python-based feature mart to support the prediction of automation rates for cash application within the Order to Cash (O2C) cycle, leveraging simulation data to enhance accuracy and decision-making.

At Accenture, I played a pivotal role in embedding data-driven thinking across the business by interpreting complex data and delivering high-impact analytical solutions that shaped financial strategies and operational performance. I developed robust data collection systems and contributed to data pipeline design, creating actionable dashboards and collaborating closely with management to align insights with strategic objectives. High-Impact Financial Modelling & Deployment: Led the full lifecycle development of critical financial models, including a Cost of Credit (CoC) forecasting system, leveraging time series analysis to provide real-time insights for operational efficiency and financial planning within the industry. This involved setting up efficient data flows to feed the models. Data-Driven Business Insights & Visualization: Conducted in-depth exploratory data analysis for an Indian NBFC, including customer segmentation to identify key client groups for strategic business development. Developed actionable Power BI dashboards to visualize these analyses, enabling informed real-time decision-making and performance tracking from an industry perspective.

Credit Risk & Operational Automation: Collaborated on building a feature mart to enhance data quality for Credit Risk Modelling, which involved data engineering practices to enable precise customer segmentation and prediction. Additionally, designed and implemented automated reporting frameworks for multiple Lines of Business (LOBs), streamlining data ingestion and processing to improve operational efficiency.

Data-Driven Startup Sourcing & Initial Vetting: Utilize predictive analytics and industry data to proactively identify high-potential startups that align with the angel's investmentthesis. Collect and analyze initial data points such as business model, team background, markettraction, early financials to rapidly qualify or disqualify leads, streamlining the angel's deal flow. Personalized Investment & Support Matching: Based on the startup's data, use analytics to recommend tailored investment structures and post-investment support. This ensures startups receive relevant resources (mentorship, network access) specificto their growth trajectory, enhancing their success rate.