Keerthi Musunuri

• Spearhead rolling out Performance Management dashboards across Finance platform
• Designing and developing descriptive analysis/data visualization on key financial indicators and present actionable insights from data to senior Finance stakeholders including CFO’s
• Working closely with the data engineering team to design and implement end to end data pipelines for processing Finance data sets housed at multiple, disparate data stores
• Conducting thorough root-cause analysis on complex data issues, proposing practical solutions that align with business objectives.
• Automated the data preprocessing pipeline using Python and Pandas, reducing data preparation time by 60% and enabling more timely data-driven decisions

Key Initiatives

• Data visualization roadmap and implementation
• BI/analytics tools/technologies evaluation and selection
• Finance Chatbot (Gen AI solutioning) – Building chatbot that can answer user queries on historical financial data (published data) using Large Language Models and Langchain
• Increased efficiency by streamlining processes and implementing new automation tools for the team which resulted in ~0.5 million $ savings/year

• Designed, developed and implemented of firm wide BI platform for Finance business users
• Implemented reporting solutions in Hadoop using Scala/Spark for Strategic Cost Management flagship projects – New Cost Allocation and Digital Value Capture
• Built and managed a team of 7 BI developers and Data engineers
• Conducted technical analysis to understand the business requirements and translated them into conceptual designs and data models
• Led BI Center of Excellence and spearheaded the Finance wide roll-out and adoption of governed, self-service analytics with Tableau
• Agile Scrum Evangelization
• Established a high-performance culture within the team by setting clear expectations and providing regular feedback on individual performance contributions.

• Delivery lead for AMENA sector, delivering Data and Analytical solutions across 6 markets covering Machine learning models and BI/Visualization
• Integration of data from 40+ source systems covering multiple subject areas such as Supply chain, Manufacturing, Products into a centralized data store (Teradata)
• Implemented an Integrated Supply Chain Project(ISCP) that leverages real-time data analytics and machine learning to optimize production and distribution of snacks product line
• This initiative improved demand forecasting accuracy by 25% and reduced lead times by 15%, enhancing overall operational efficiency
• Execute activities related to end-to-end project management including project plans and estimates, scoping and requirements through implementation and deployment

• Managed a team of 13+ staff across Hyderabad, Bangalore and Gurgaon
• Developed an Infrastructure Cost Optimization model utilizing machine learning algorithm to analyze resource usage patterns and identify inefficiencies by examining historical data on CPU, memory, and storage utilization
• By implementing a centralized tracking tool, the project achieved a 40% reduction in unused infrastructure costs per year, translating to around $5 million USD
• Worked closely with the CIO and the CFO organizations to strategize the Infrastructure roadmap for the organization
• Provide data, analysis and commentary on management KPIs and strategy execution

• Deliver people story through data discovery, effective visualization and actionable insights to Global Heads
• Developed an employee attrition predictive model using logistic regression to predict which employees were at risk of leaving the company within the Private Banking division
• This model led to successful retention of 60% of at-risk employees by implementing retention strategies like adjusting deferred cash bonuses, offering additional trainings etc
• Developed data analysis tool to track and analyze new hire recruitment metrics like time-to-offer, time-to-hire, acceptance ratio etc
• The tool helped to reduce candidate drop-off rates by 20% and improved overall candidate satisfaction rate
• Promoted a data governance strategy (Collibra) across the organization in an effort to align metrics and communicate data changes to reporting and the data warehouse teams
• Implemented CDO governance in measuring various KPI’s such as quality, completeness, Robustness & applicability etc

• Enhanced transaction monitoring systems by integrating machine learning algorithms that analyze large datasets for anomalies indicative of financial crime
• The implementation led to a 35% improvement in the identification of suspicious activities, allowing for quicker investigations and better resource allocation within compliance teams
• Leveraged Data visualization techniques like heat maps to analyze transaction volumes across different geographic regions
• This enabled pinpoint areas with unusually high transaction volume that could indicate financial crime or other fraudulent activities
• Transitioned various excel based reports to business intelligence tools like OBIEE and Tableau