● Analyzed comprehensive datasets to uncover trends and patterns, informing strategic business decisions and contributing to a 10% increase in operational efficiency.● Engineered and optimized reporting dashboards and KPIs, enhancing decision-making processes and improving operational efficiency by 15%.● Collaborated with various departments to translate business needs into technical specifications, resulting in data solutions that boosted productivity by 25%.My roles involve:● Data Analysis: Leveraged Python, SQL, and Microsoft Excel to conduct in-depth data analysis, identifying key trends and extracting actionable insights that led to a 5% increase in customer retention.● Data Cleaning and Preparation: Ensured data accuracy and integrity through meticulous cleaning and preparation, enhancing data quality by 20%.● Data Visualization: Crafted engaging and interactive dashboards using Power BI, improving stakeholder communication, and facilitating a 30% faster understanding of data insights.● Continuous Learning: Committed to ongoing professional development in data analysis and machine learning, staying ahead of industry trends and applying new knowledge to optimize data processes.● Cultivated relationships with clients by demonstrating in-depth product knowledge.● Translated technical data analysis concepts into compelling sales narratives, resulting in a 25% uplift in sales conversions.● Navigated through complex sales cycles, leveraging data analytics to tailor solutions.● Conducted client needs assessments using data-driven methodologies, ensuring a perfect alignment between product offerings and client requirements, which boosted customer satisfaction scores by 40%.● Facilitated training sessions for clients on data analysis tools, enhancing their understanding and enabling a 35% improvement in their operational efficiency.

• Recognize Market Opportunities.• Forming and orchestrating the team.• Performing direct Marketing and Promotion launching.• Create Business Plans and legal Documents.• Developed a data analysis project during coursework, analyzing sales data to identify trends and recommend strategies for improving profitability.• Utilized Python and SQL to extract, transform, and load data from multiple sources for analysis, resulting in improved data accuracy and efficiency.• Created interactive dashboards using data visualization tools to present key performance metrics and trends to stakeholders.• Determined ERP and CRM need via consultation, business process analysis, and observations.• Understood the different departments and processes such as CRM, Production, Manufacturing, HR Management, Supply Management, Finance, Product Lifecycle Management, Inventory, Procurement, etc.• Provided ERP end-user training and technical support.• Software using: - ERPNext, Zoho CRM, Hubspot, Power BI

Utilized SPSS statistical software and python to manage data, perform analyses, and create visual representations of data.Checked the probability of output using different statistical studies and interpret the result of statistical analysis.Understand the relationship among major statistical concepts (e.g., Type I and II errors, power alpha, effect size, sample size). Calculate a variety of statistical tests (e.g., s-score, t-test). Conducted hypothesis tests. Discussed the strengths and weaknesses of statistical tests. Performed advanced 100% comprehensive business analysis using statistical analysis, explanatory and predictive modeling.

Objective and Scope of the project: Thus, the main objective of this project is to predict the insurance premiums of the member based on the behavioural data collected from the individuals and also by analyzing the future claim chances of the member so that insurance companies can make useful and accurate predictions. Based on these predictions, they can then evaluate the customer with following decisions and make better customer services. judgement calls: • After analyzing the customer insurer can suggest which individuals deserve which kind of insurance plan? • Based upon the analysis on individual’s characters, predicting their health insurance premium.Specifically, this paper aims to investigate how machine learning methodologies can improve policyholder retention and conversion estimation over that of classical.Project Methodology and Design: The main goal of the project is to predict the health insurance premium cost based upon customer details.Predictive Modelling: Predictive Modelling is a machine learning method which is the process of uncovering relationships within the data by using a mathematical model for predicting some desired output. It uses historical data to make an appropriate model and using this model unseen data is predicting.The goal is to predict the health insurance premium cost which is a numeric outcome. So, regression models like multiple linear regression, SVR, random forest and Gradient Boosting Regression are used for predictive modelling.Conclusions: In this thesis work, four different regression models were worked with historic data, and the predicted premiums from these models were compared with actual premiums to compare the accuracies of these models with the help of different evaluation metrics. It has been found that Random Forest is the best performing model The models can be applied to the data collected in coming years to predict the health insurance premium.

• Lead and oversee a team of underwriters and operations staff, ensuring adherence to underwriting guidelines, compliance with regulations, and consistent decision-making.• Collaborate with sales teams, insurers, and agents to provide technical expertise, resolve complex inquiries, and support customer interactions.• Monitored claims data, identify trends, and make recommendations for adjustments to underwriting guidelines to mitigate risk and reduce losses.• Stay updated on industry regulations, healthcare legislation, and privacy requirements, ensuring compliance and implementing necessary adjustments.• Monitored the medical team, overseeing the hiring, training, and professional growth of the members.• Provided leadership and direction for employees, supervising activities to drive productivity and efficiency.• Reviewed and evaluated new and renewal applications to determine insurance risk and premium. Quotations are taken from insurance companies, comparing the quotes, and advising the best to the client. Alsonegotiated with the insurer for premiums if required• Prepared relevant reports for management and account team as per their requirement and participated in monthly review meetings withmanagement.• Technical proficiency and analytical skills.

Assistant Systems Engineer - Tata Consultancy Services, India.Designed Portfolio Management Tool (PMT) for Bank of America.PMT is a web based tool designed to support portfolio and organization managementroutines. Java web based technology coupled with oracle database environment andonline reporting using Crystal reports.Skill/Tools used were JAVA, Struts1.3, Hibernate, Crystal Reports, and DHTML.Duties:· Designing, development, testing & production support of JAVA Applications.· Participating in discussions with clients to clarify what they need.· Designing prototypes for websites.· Developing and maintaining the front end functionalities of websites.· Creating logos, banners and buttons for websites.· Writing cross-browser XHTML, CSS & JavaScript.· Simultaneously managing several databases and reporting tools.· Performed maintenance and updates to existing client Web sites.· Documented software and technical specifications.· Specifying system changes that meet a client’s requirements.· Investigating, fixing and debugging software defects.· Provided guidance and advice to less experienced staff.· Escalated operational issues to senior management.· Testing a website before it goes live to identify any problems.· Preparation of test plans (manual testing), test cases and execution & reporting.Ability to write test cases and test scenario, review, run and generate test reports.· Providing technical support to end users.