Bhagyashree Swain

• Streamlined communication workflows, improving customer satisfaction by 20% and operational efficiency by 15%. • Maintained databases and automated data tasks with Excel and Power BI, reducing manual effort by 30%.

At Amazon, I led data-driven initiatives to streamline product operations and enhance customer satisfaction. - Automated 3D product image processing with TensorFlow, cutting manual intervention by 70% and improving accuracy by 15%. - Reduced product return rates by 10% by implementing advanced computer vision techniques using CNNs and OpenCV. - Deployed scalable, real-time processing pipelines using AWS Lambda and S3, ensuring operational efficiency for high-resolution images. - Regularly analysed performance metrics, introducing process enhancements to eliminate bottlenecks and boost productivity.

I enabled organisations to harness their data, improve cash flow, and make data-driven decisions by designing end-to-end solutions. - Developed a GBM model to predict payment delays, boosting on-time payments by 0.6% and strengthening client relationships. - Designed and integrated interactive Tableau dashboards with SQL, reducing reporting time and increasing efficiency by 20%. - Partnered with cross-functional teams to optimise data workflows and improve operational processes, directly influencing business decisions.

During my time at Merkle, I focused on improving customer engagement and ROI for a major global client. - Led a customer segmentation project using clustering algorithms, increasing engagement by 15% for PayPal. - Designed a logistic regression model that improved advertising ROI by 20% through better targeting strategies. - Developed real-time dashboards to track performance metrics, reducing campaign response time by 25%.

As a Data Science Associate, I worked on optimising conversion rates and enhancing customer retention through predictive models. - Created a regression-based prediction model for Total Online Value, delivering a 35% increase in conversion rates. - Reduced churn and boosted retention by 1.5x using an XGBoost-based customer attrition model. - Automated manual reporting tasks with Python and SQL, saving over 40% of reporting time and improving data accuracy.

• Improved lead conversion rates by 25% through an SQL-based lead scoring model. • Enhanced customer retention by 15% by providing actionable insights into user behaviour and enabling personalised outreach.

Dissertation Project: Awarded distinction for designing predictive models to optimise fertiliser management, utilising EWC and Fisher Information Matrix to prevent catastrophic forgetting and improve model accuracy by 15%. Team Project: Conducted a sensitivity analysis on cardiovascular models to provide insights for optimising cardiovascular disease treatments.