Data Analyst, Healthcare, Insurance, Financial Services
Designed and built interactive dashboards using Python and Excel, providing real-time visibility into business performance for executive leadership, resulting in 10% effectiveness in informed decision-making.Led the data mapping process, determining the required mappings to join various healthcare datasets.Implemented campaign impact evaluation methodology, analysing key metrics such as conversion rates and ROI, leading to a 20% increase in marketing effectiveness.Developed and executed a comprehensive machine learning model using pyspark and propensity modelling to identify customer churn patterns, resulting in a 15% reduction in churn rate.Employed clustering and regression algorithms to analyse customer satisfaction scores and call retention and strategically target customers for specialised marketing, resulting in a 15% acceleration in customer engagement.Provided guidance and training to new team members on ongoing projects, promoting a positive work culture and ensuring successful project completion, updated JIRA with daily tasks, and documented processes on Confluence.Incorporated Python (Pandas, NumPy) to retrieve, process and analyse complex datasets from data warehouse on healthcare insurance data, identifying 8 key performance indicators (KPI) and patterns for client & non-finance stakeholders' enhancing decision-making by 10%.Built and executed comprehensive communication strategies to regularly update partners, non-finance stakeholders, clients, and senior leadership on project progress, resulting in a 20% increase in client satisfaction scores.Collaborated with cross-functional teams to analyse, automate, organise, deliver and forecast financial reports using statistical analysis and regression models, resulting in a 12% reduction in expenses.Represented in a global cross-matrixed environment across 4 time zones with remote business users for the US healthcare Client, enhancing project efficiency by 10%.
