Jyosmitha Munnangi

Client Name: Ameritas Insurance Corp.

Project Name: Policy Persistency - Individual.

Project Description:

· This project aims at understanding what are the factors that contribute towards policy attrition for Individual Products (Life Insurance, Disability Insurance & Annuities) and create a Machine Learning model which predicts if a policy would fall into low medium and high risk categories.

Responsibilities:

· Aligned with business to define problem statements and objectives, fostering targeted data-driven solutions.

· Collaborated in requirements sessions, crafting hypotheses and solutions alongside customers.

· Orchestrated data extraction alongside Business Analysts & Data Engineering team.

· Transformed data through cleansing, manipulation, and new feature generation, ensuring model compatibility.

· Performed Hypothesis testing on the assumptions that can potentially lead to policy lapsations.

· Executed Exploratory Data Analysis to unearth actionable insights and improvement avenues.

· Developed, tested, and fine-tuned Machine Learning models for optimal performance.

· Engineered a Minimum Viable Product and automated Dataiku pipelines for streamlined file processing.

· Implemented MLops framework, enhancing model monitoring, auto-retraining, and performance assessment.

· Established predictive model explainability mechanism, offering customers directional insights and explanations for predictions.

Client Name: Ameritas Insurance Corp.

Project Name: Enterprise Insights – Individual, Underwriting Analytics

Project Description:

· Automated underwriting risk assessment by considering applicant demographics, policy details, and financial status.

· Implemented risk categorization (low, medium, high) to expedite application processing and enhance efficiency.

· Developed a dashboard to capture vital business metrics, providing insights into application approval stages.

Responsibilities:.

· Tested data quality and granularity to ensure accurate extraction.

· Identified key underwriting factors, influencing application approval decisions.

· Conducted data analysis, translating findings into insightful charts.

· Developed prediction models to classify the applications into low, medium, high risks.

Client Name: Ameritas Insurance Corp.

Project Name: Customer Insights – Individual Products

Project Description:

· Analyzed Individual Products' performance by comparing 12-month rolling windows, providing insights on demographics, preferred products, and distribution channels via Power BI.

· Enabled targeted marketing, cross-selling, and upselling efforts for improved customer engagement and business growth.

Responsibilities:

· Brainstormed with business stakeholders for understanding business needs and define wireframes and relevant metrics using Miro whiteboard.

· Prepared data for modelling by cleansing, manipulating, and generating new features.

· Conducted Exploratory Data Analysis (EDA) to generate actionable insights and improvement ideas.

· Developed a segmentation model to categorize sponsors into 5 segments, enhancing performance understanding.

Client Name: Vodafone Ziggo

Project Name: TCS Digital TwinX

Project Description: Predictive model which calculates the cross sell and upsell opportunities for Vodafone Fixed and model lines across enterprises.

Responsibilities:

· Collaborated with customers, stakeholders, and business analysts to understand and excel in Exploratory Data Analysis (EDA) for diverse projects.

· Successfully executed the G2M (Go-To-Market) module across multiple telecom clients.

· Boosted upselling and cross-selling of customer products by 30% using machine learning techniques, driving business recommendations.

· Interpreted complex Blackbox models with AI explainability tools to generate actionable insights.

· Crafted impactful Tableau dashboards showcasing customer KPIs, earning customer recognition.

Client: Bank of America

Project Name: Enterprise Datawarehouse Management

Project Description: Development, Support, Maintenance of Mainframe and Teradata Applications and supporting datamover jobs across platforms.

Responsibilities:

· Developed complex queries for data loading and validation using Teradata scripts.

· Conducted root cause analysis (RCA) to identify issues in Test/Production environments, ensuring successful production phase transitions.

· Initiated value-added analyses that improved space forecasting, preventing job failures.

· Led successful software and hardware upgrades, coordinating impact jobs and application dependencies.