Sidthaarth Gnanasekar Email & Phone Number

Leeds, GB

• Built and optimized XGB regressor and GLM models to predict average claim size, forecast claim frequency, and anticipate customer retention ratio, achieving R² scores of 0.90, 0.89, and 0.93 respectively, by.
• Utilized claims severity, retention, and claims frequency models to project loss ratios till FY-28, delivering actionable insights to support strategic decision-making and financial forecasting
• Created various charts, measures, and tables to showcase exploratory data analysis and model validation to stakeholders. Gained expertise in DAX functions and Power Query Editor for efficient data transformation
• Proficient in writing complex SQL queries, including window functions, joins, and subqueries, to efficiently extract and manipulate data from Oracle databases, supporting data-driven insights and analysis
• Optimized policy sales profitability by designing and implementing a data-driven website for agents, enabling the identification and promotion of high-performing policies, which increased company profitability by 3%.

London Area, United Kingdom

• Developed XGBoost Regressor to forecast Existing Customers Marginal Utilisation and evaluate their eligibility for Credit Limit Increase (CLI), aiming to enhance customer satisfaction and minimize credit risk. Led to.
• Leveraged Tableau extensively to create multiple dashboards for real-time monitoring of Model Performance Metrics, Fairness Assessment, Model Calibration and features’ Population Stability Index (PSI), updated monthly.
• Proficient in managing end-to-end project pipelines encompassing Data Extraction from Redshift database, Pre-processing, Exploratory Data Analysis (EDA), Feature Engineering, Model Building, Model Tuning using Grid.

Leeds, England, United Kingdom

• Created clustering algorithms to optimise product store assortment at store locations, led to 7% increase in revenue
• Collaborated with merchandising team to develop data-driven assortment plans, resulting in 10% decrease in excess inventory and 8% improvement in inventory turnover
• Analysed customer transaction data to identify cross-selling and upselling opportunities, leading to 12% increase in average transaction value and conducted A/B testing on promotional campaigns to evaluate their.
• Built interactive dashboards and reports to track key retail metrics, such as sales performance by product category, store location, and customer segment, leading to improved decision-making processes
• Developed and maintained data ingestion Extract, Transform, Load (ETL) pipelines in Azure Data Factory resulting in improvement in speed and accuracy; maintained robust pipelines for CI/CD deployment

London, England, United Kingdom

• Developed customised ML algorithms to predict customer churn score of 90% and identify the underlying reasons for customer attrition in the business with a specific focus on Insurance products
• Utilised random undersampling, oversampling, and cross-validation techniques which effectively detected and reduced fraudulent activities by 9% in Insurance and claims; bolstered security and integrity of business.

Chennai, Tamil Nadu, India

• Performed requirements analysis, design, and prototyping, demonstrating expertise in Power Query Editor, DAX functions, data modelling, and F-measure, resulting in a significant 25% reduction in report generation time
• Leveraged advanced analysis actions, calculations, hierarchies, detail-level summary reports, and KPI dashboards to develop comprehensive management dashboards, financial reporting, and sales business intelligence.