In the competitive world of data analytics and risk management, Harshita Cherukuri stands out as a beacon of innovation and excellence. With a career spanning major financial and tech companies, Harshita has made significant contributions to optimizing strategies, reducing costs, and improving efficiencies. Her journey from a Mechanical Engineering graduate from NIT Warangal in India to Strategy Optimization Manager at Barclays in Dallas is a testament to her relentless pursuit of knowledge and her ability to leverage data to drive impactful business decisions. In this exclusive interview, we explore Harshita’s career, her achievements, and the insights that have shaped her path.
Q1: Can you share your journey from studying Mechanical Engineering in India to becoming an Assistant Vice President at Barclays?
A1: My journey began at the National Institute of Technology, Warangal, where I pursued a Bachelor’s degree in Mechanical Engineering. Despite my technical background, I was always intrigued by data and its potential to solve real-world problems. With a solid foundation in both engineering and data analytics, I joined Amazon as an Operations Manager, hands on experience with data analysis and reporting sparked broader applications of information technology in business management. This curiosity led me to the University of Texas at Dallas, where I completed my MS in Information Technology Management. My career trajectory then took me to JP Morgan Chase, where I focused on risk management and collections strategies for Home Lending portfolio exceeding $800 billion. Currently, as an Assistant Vice President at Barclays, I lead strategic initiatives that integrate advanced data analysis and predictive modelling to drive business value.
Q2: What inspired you to transition from Mechanical Engineering to Information Technology Management?
A2: The transition was driven by my fascination with data and its transformative potential. During my undergraduate studies, I realized that the ability to analyze and interpret data could significantly enhance decision-making processes. This realization motivated me to pursue a master’s degree in Information Technology Management, where I could merge my technical background with advanced data analytics skills. The combination of these disciplines has enabled me to approach problems from a unique perspective and develop innovative solutions that drive business success.
Q3: Can you discuss some of the major achievements in your role at Barclays?
A3: One of my most notable achievements at Barclays was relaunching text messaging for Collections after a 13-year hiatus. This initiative, which leveraged risk based model score, resulted in $40MM in impairment savings. Additionally, I developed and managed comprehensive Management Information (MI) reports for collections and operations, which significantly improved our strategic decision-making processes and understand process effectiveness and efficiency. Another key accomplishment was refining existing credit and operational strategies through predictive modeling techniques, which supported strategic recommendations for management and led to substantial cost savings.
Q4: How have your technical skills contributed to your success in data analysis and risk management?
A4: My technical skills have been instrumental in my success. Proficiency in programming languages like SQL, Python, and SAS, coupled with expertise in data analysis tools such as Tableau and Alteryx, has enabled me to perform complex data extractions, statistical analyses, and predictive modeling with precision. These skills have been critical in extracting actionable insights from large datasets and creating impactful visualizations that communicate key findings to stakeholders. Moreover, my ability to optimize ETL processes and build predictive models has been pivotal in driving efficiency and effectiveness in various projects.
Q5: What was your role in developing machine learning models at JP Morgan Chase, and what impact did they have?
A5: At JP Morgan Chase, I played a crucial role in developing and maintaining machine learning models for generating segmentation strategies and forecasting delinquency probabilities. These models allowed us to sub-segment customers based on digital proclivity, which supported the implementation of digital segmentation strategies and resulted in $1.8MM in operational cost savings. Additionally, the models helped in reducing delinquency rates by 200 basis points, contributing to the overall stability of our Home Lending portfolios. The ability to predict customer behaviour and tailor our strategies accordingly had a profound impact on our risk management efforts.
Q6: Can you describe a challenging project you worked on and how you overcame the obstacles?
A6: One of the most challenging projects I worked on was improving salability metrics at Amazon. The goal was to convert unsellable units to sellable disposition, which required a comprehensive analysis of past data and the development of effective productivity plans. The primary challenge was ensuring customer expectations and accuracy during the transition from one fulfilment center to another. By leveraging my skills in SQL and statistical analysis, I was able to design and execute a successful plan, resulting in a significant YTD improvement in salability metrics from 30.64% to 90.27%, saving $1.4MM in the process.
Q7: How do you approach the development and management of comprehensive Management Information (MI) reports?
A7: Developing and managing MI reports involves a meticulous process of data extraction, validation, and analysis. I start by identifying the key metrics and performance indicators that align with our business objectives. Using tools like Tableau and Power BI, I create detailed visualizations that highlight these metrics and provide insights into our collections and operations performance. The reports are designed to be easily interpretable by stakeholders, facilitating informed decision-making. Regular updates and iterative improvements ensure that the reports remain relevant and continue to drive strategic initiatives.
Q8: What role do predictive modeling techniques play in refining credit and operational strategies?
A8: Predictive modeling techniques are essential in refining credit and operational strategies. By analyzing historical data and identifying patterns, we can forecast future trends and make data-driven decisions. For instance, at Barclays, I utilized predictive modeling to support strategic recommendations for management. These models helped in identifying high-risk accounts, optimizing collection strategies, and improving overall operational efficiency. The ability to predict outcomes with a high degree of accuracy allows us to proactively address potential issues and enhance our strategic planning processes.
Q9: How have you utilized data visualization tools to communicate key insights to stakeholders?
A9: Data visualization tools like Tableau and Power BI are invaluable for communicating key insights to stakeholders. These tools allow me to transform complex data into intuitive visual representations, making it easier for stakeholders to understand and act upon the information. By creating interactive dashboards and reports, I can highlight trends, correlations, and anomalies that might otherwise go unnoticed. Effective data visualization not only facilitates better decision-making but also fosters a data-driven culture within the organization, encouraging stakeholders to leverage data in their strategic initiatives.
Q10: What advice would you give to aspiring data analysts and risk management professionals?
A10: My advice to aspiring data analysts and risk management professionals is to continuously hone your technical skills and stay abreast of industry trends. Developing proficiency in programming languages, data analysis tools, and statistical techniques is crucial. Additionally, focus on gaining a deep understanding of the business domain you are working in, as this will enable you to provide more relevant and impactful insights. Networking with industry professionals and seeking mentorship can also provide valuable guidance and opportunities for growth. Finally, embrace a mindset of continuous learning and innovation, as the field of data analytics is constantly evolving.
Harshita Cherukuri’s career is a shining example of how a strong foundation in technical skills, combined with a passion for data-driven decision-making, can lead to remarkable achievements. From her early days as an engineering student in India to her current role as an Assistant Vice President at Barclays, Harshita has consistently demonstrated her ability to drive innovation and deliver impactful solutions. Her journey inspires aspiring professionals to pursue excellence, embrace challenges, and leverage data to create value. As Harshita continues to break new ground in the field of data analytics and risk management, she remains a testament to the power of perseverance, continuous learning, and strategic thinking.
