Bhavana Reddy Chadagonda is a seasoned analytics professional with over 10 years of experience driving strategic business solutions through data interpretation, experimentation, and insights generation. Currently serving as a Staff Data Scientist at Intuit, she specializes in building reliable and trustworthy products that deliver measurable business impact. She has established herself as a leader in the analytics space, with deep expertise in A/B testing, causal inference, and machine learning. Her technical proficiency spans a comprehensive toolkit including Python, SQL, R, Tableau, and advanced statistical modeling techniques, and she has consistently demonstrated her ability to translate complex data into actionable business strategies across diverse industries.
At Intuit, Bhavana has spearheaded transformative initiatives that have generated significant revenue impact. She led the development of a personalized AI-driven signup flow that increased subscriptions by 3% and generated $15M in annual revenue. Her innovative approach to connecting online and offline channels through end-to-end dashboards has provided unprecedented insights into user journeys and optimization opportunities. She has also played a crucial role in advancing experimentation capability in the paid media team by creating long-term roadmaps and implementing new testing methodologies, including sequential/Bayesian testing approaches that have accelerated decision-making while reducing costs.
Prior to Intuit, Bhavana made substantial contributions at Twitter's Health team, where she designed groundbreaking content reporting flows that delivered a 50% increase in actionable reports and $1.5M in revenue impact. Her work in building measurement frameworks for automated remediation systems resulted in a 7% increase in automated responses and significantly improved platform safety. She also collaborated with cross-functional teams including Engineering, ML, and Legal to reduce report review SLA from 72 hours to 32 hours by optimizing ML ranking models.
During her tenure at Glassdoor, she revolutionized experimentation capabilities by automating A/B test analysis, reducing analysis time from 2 days to 2-4 hours while achieving a 25% month-over-month lift in content contribution and a 10% increase in retention. Her strategic redesign of the platform's "give-to-get" content model led to a 20% increase in user-generated content without compromising long-term engagement. She also built comprehensive pipelines and dashboards to monitor content moderation metrics, reducing manual analysis by 70%.
At Domino's Pizza, Bhavana developed predictive models that identified key service metrics driving sales growth, creating tools that enabled over 5,000 stores to maximize performance and estimated service improvements that could drive 10-12% sales growth at the national level. Her personalized advertising strategies contributed to a $20M annual revenue increase through improved customer lifetime value predictions using probabilistic models.
Bhavana holds a Master's degree in Business Analytics and Project Management from the University of Connecticut School of Business and a Bachelor's degree in Electrical and Electronics Engineering from Jawaharlal Nehru Technological University, India. This unique combination of technical engineering foundation and business analytics expertise enables her to bridge the gap between complex technical solutions and strategic business outcomes.
Throughout her career, Bhavana has consistently delivered results that matter, generating over $36M in measurable revenue impact through her analytics initiatives, improving operational efficiency by up to 70% through automation, and advancing experimentation capabilities across multiple organizations. Her approach to analytics is characterized by her ability to influence product roadmaps, drive cross-functional collaboration, and translate data insights into strategic business actions. Her expertise in both technical execution and strategic thinking makes her a valuable leader in the evolving landscape of data-driven decision making.