Welcome to my page! My name is Kushal , I’ll take you to the alternate world of Data
Iam a contemplative analyst, inspired by tough problems.have worked with automotive giant Toyota.have also been with insurance leaders LexisNexis
Stick around to see some of my work.
About me
As a Data Scientist, I apply my Machine Learning and Cloud Technology skills to solve challenging business problems and deliver data-driven solutions. I have developed product strategies, detected anomalies, and designed A/B testing models to optimize user experience and revenue growth. I am passionate about exploring Data Science and learning new tools and techniques to enhance my analytical capabilities.
I have a Master's degree in Business Analytics from The University of Texas at Dallas, where I graduated with recognition for my academic excellence. I also have a Bachelor's degree in Computer Science from the International Institute of Information Technology Bhubaneswar. During my studies, I gained hands-on experience in data regression, sentiment analysis, and SQL optimization through various internships and projects. I am a fast learner, a great team player, and motivated to succeed in my career.
Work Experience
July 2023 to Feb 2024
- Accomplished an innovative product recommendation system for Toyota’s buyers utilizing clustering analysis techniques. This system enabled precise customer segmentation to target upselling and cross-selling strategies effectively, resulting in 15% increase in successful sales conversions.
- Conducted deep dive to uncover trends and correlations to investigate analysis for customer churn, recognized opportunities, and led to implementation of the proposed ideas to lower variable costs by 20%, improved operational efficiency.
- Developed dashboards utilizing Tableau and SQL for Toyota’s mobility team that highlighted driver behavior across regions, violation types among drivers and introduced quality checks through scikit-learn
May 2022 -July 2022
- Generated ad-hoc reports in Excel to convey insights and recommendations to stakeholders and conducted t-tests to extract information on property type comparisons, panned out in 3% improvement in accuracy.
- Redesigned business cases to target SME’s (low and medium enterprises) by employing K-Means Clustering for customer segmentation, resulting in a 5% increase in business performance across targeted enterprise groups.
- Enhanced project scope for effective implementation and business by redesigning PD (Probability Default) documentation, ensuring alignment with low and medium-income community outreach
Feb 2019- July 2021
- Monitored and analyzed network traffic for Barclays Banking Division in India and Africa, identified bottlenecks and optimized resources to prevent network downtime.
- Leveraged data related to third-party value-added services (VAS) for Barclays using Excel’s VBA, macros, charts, and graphs to offer strategic business recommendations that led to streamlining the workflow
- Automated sales data pipeline using SQL on multiple datasets with over 160k rows, which reduced the time spent manually creating dashboards by 94%.
- Implemented MS Access to design and build relational databases to manage and analyze fixed income analysis for the operational accounting team, resulting in increased efficiency by 60%.
Academic Projects
- Engineered real-time fraud detection system using Logistic Regression for finance dataset targeting AMEX credit card, reduced fraud rate by 12% with thorough documentation and re-evaluation with professor.
- Led the final presentation with ad-hoc analysis in credit insurance, leveraged fraud detection skills to optimize and mitigate risks.
- Engineered a data-driven approach focused on understanding consumer preferences and behavior patterns for Abbott Nutrition's healthcare products and explained it to the stakeholders.
- Directed a data-driven initiative to analyze driver behavior and mitigate accident risks among large commercial trucks in California. Implemented targeted measures resulting in a 25% reduction in accident frequency within the first year, to focus on enhancing road safety.
- Applied Decision tree based ensembled model to assess risk factors associated with drivers, including events, total distance traveled, average speed, and mileage. Achieved a notable 30% decrease in high-risk driving behaviors among identified drivers, contributing to decrease in accidents statewide.
- Developed a real-time big data analytics platform for California truck emissions using Apache Spark and Hadoop, leading to a 20% reduction in emissions violations.