Sehani Arumapperuma

• Planned, designed, and implemented surveys and data analytics solutions. • Worked collaboratively with stakeholders to develop strategies and methodologies. • Displayed effective communication, interpersonal, and leadership abilities coupled with problem-solving, conceptual thinking, quantitative,qualitative, and analytical skills

• BI Track lead of Mobile Prepaid and Digital Services Divisions: • Contributed to the Hyper-Personalisation Project to recommend the best offer to the customers on their best occasion. • Synthesised research findings in easy-to-understand terms for the business. • Recommended resource requirements and collaborated with relevant stakeholders. • Provided ongoing tracking and monitoring of the performance of decision systems and statistical models. • Provided guidance to achieve consistent commercial growth and execution excellence in the division. • Managed operational report requests via Tableau. • AWS Deep Racer 2022 : 1st runners up of the AWS Deep Racer, Organized by Axiata Groups

• BI Track Lead of Enterprise SOC division: • Contributed to the cloud migration. • Developed statistical and machine learning models, validated results, and finalised appropriate models for deployment. • Evaluated business process integration options, offered design perspectives, conducted analysis for successful business deployment, and presented findings and actionable intelligence to senior management. • Defined activities, strategic direction, scope, and timelines on data science projects. • Developed dashboards to cater to divisional business requirements. • Maintained documentation of methodology, source codes, version control & other best practices in development operations. • Adhered to agile development practices.

Next Best Offer Prediction Model (Tier Point NBO) Developed a collaborative filtering recommendation model to recommend next best offer to the premium loyalty customers and developed a tableau dashboard to get insights of the results. Franchise Outlet Performance Dashboard Developed the dashboard on Tableau to monitor the performance of the outlets.

• Performed data analysis, pre processing, and wrangling using advanced Excel and SPSS. • Developed Power BI dashboards to visualize production insights and identify trends and patterns. • Conducted data entry tasks, ensuring data accuracy and up-to-date records.

Modules Covered: Machine Learning and Artificial Intelligence, Database and Data Management, Data Analytics and Visualisation, Applied Data Programming Research: Churn Prediction Model for Telecommunication Customers Developed a binary classification model using machine learning techniques to predict customer turnover, along with a user-friendly dashboard implementation to provide easy access to the model's results and detailed insights. (Logistic Regression, SVM, Decision Tree, Random Forest, Adaboost Classifier, Extra Tree Classifier, Gradient Boost, Tableau) Academic Projects: Medical Image Analysis and Classification (Neural Network, DNN, CNN)

Modules Covered: Applied Statistical Models, Data Science, Statistical Programming, Operational Research, Applied Time Series, Applied Multivariate Methods, Risk Management, Database System, Actuarial Mathematics Final Year Research: Recommendation Engine for downgraded customers of Telecommunication Companies (Collaborative Filtering Recommendation System, K - prototype Clustering)