Anish Goel

• Data Governance & Business Intelligence Alignment – Led governance and reporting standardization for NICE CxOne launch, acting as an SME for Workforce Management, Quality Management, and leadership teams. Trained 25+ users on reporting capabilities and ensured seamless data integration for call center operations. • Data Strategy & Financial Modeling – Built the company’s first financial cost model, integrating costs and revenue streams to calculate net profit and cost of ownership. Identified a $1.5M Net Income opportunity, presenting insights to Senior Leadership to optimize platform selection for Flight Ticketing. • Data Reconciliation & Contract Optimization – Automated Python-driven reconciliation of supplier billing with internal accounting to ensure billing accuracy. Identified system inefficiencies, renegotiating contracts and saving $1.1M in FY 2023. • Self-Service Payment Analytics & Digital Transformation – Enhanced Google Analytics 4 tracking for Cruise self-service payments, implementing Tealium tags to monitor customer journeys and pain points. Drove a 10% YoY increase in digital payments, reducing call center dependency and saving $350K in FY 2024. • Loyalty Program A/B Testing & Revenue Optimization – Conducted A/B testing on point-based flight bookings, using Python for trend analysis. Reduced redemptions by 70%, shifting loyalty points to higher-margin products, saving $1.8M in FY 2023. • Omni-Channel Analytics & Customer Experience Optimization – Developed a Power BI dashboard integrating calls, chats, bookings, web, and CSAT metrics, using SQL to analyze CRM settings for chatbot scope. Optimized engagement, shifting from 70% calls to 35% calls, 30% bot-contained chats, and 35% agent chats, saving $250K in 2024, with $1.3M projected savings in 2025. • Leadership & SME Responsibilities – Served as SME for operational data, guiding teams on leveraging Power BI and structured datasets for business insights.

• Product Performance & Executive Reporting Dashboard – Designed a Power BI dashboard integrating web traffic, bookings, revenue, and customer interactions for real-time KPI monitoring, optimizing executive reporting by structuring data to track performance trends and improve data-driven decision-making on Travel products.

• ETL Pipeline Development & Power BI Integration – Extracted petabyte-scale data from AWS S3, transformed it using Python scripts orchestrated through AWS Glue, and loaded the refined data into Amazon Redshift, enabling enterprise-level reporting through Power BI, combining online traffic and offline purchase data to deliver unified marketing performance insights. • Retail Performance & Marketing Analytics – Developed a Tableau dashboard with drill-through functionality, interactive filters, and trend analysis, enabling marketing to track top/bottom-performing products, refine targeting strategies, and reduce ad-hoc requests by 50%. • Predictive Analytics & Pricing Strategy – Deployed a Random Forest Model to analyze customer behavior and predict optimal pricing for repeat orders while preserving margins, generating two-week stock forecasts to optimize inventory planning and align pricing with demand.

Identified steel plate defects using non-destructive testing (NDT) and provided optimised solution using regression analysis

Reduced aluminium loss by 6% by developing a simulation model to enhance casting process

• AirBnB Listing Price Prediction: Employed business expertise gained through extensive consultation with a real estate Subject Matter Expert. Utilized XgBoost, Random Forest, and Neural Networks models, with XgBoost selected as the optimal model with an R2 score of 0.66, achieving a balanced bias-variance trade-off for precise price predictions. • Credit Card Default Prediction: Implemented Machine Learning models such as Generalized Additive, Gradient Boost, and AdaBoost models for next-month default prediction. Utilized Domain knowledge and expert insights to set an asymmetric cost and cut-off probability of 1/6, selecting AdaBoost as the best model with an out-of-sample AUC of 0.77.

• Forecasting Annual Birth Rate in India: Built ARIMA models based on ACF/PACF plots and optimized the Facebook's Prophet model through hyperparameter tuning to achieve an RMSE of 0.73 for accurate annual birth rate forecasts. • Analysis of European Football Leagues: Utilized MS-SQL server to classify players of Top-5 European Leagues from 102 nations into Top Goal scorers, Enablers, First Line of Defense and Last defender. Built dynamic dashboard using Tableau for a comprehensive league wise player analysis categorizing them into Attackers, Midfielders and Defenders.

* Represented NIT Raipur in field and track events at Inter-NIT meet * Core Member at Sahyog- The Mentorship Club, NITRR * Publicity Coordinator at Asia's second largest cultural fest, Eclectika * Organised various blood donation camps