DIVYA SUSHMITHA REGIDI

London,United Kingdom

Summary

Quality-focused professional with a background in ensuring product and process compliance in regulated environments. Enhance operational efficiency and product quality through rigorous testing and continuous improvement initiatives. Leverage analytical skills and keen attention to detail to identify and resolve quality issues swiftly.

Overview

years of professional experience

years of post-secondary education

Work history

Software Quality Assurance Associate

Telus International

Noida, India

2022.11 - 2024.10

- Worked extensively on Salesforce-based enterprise applications, ensuring functionality, data accuracy, and system reliability.
- Designed and executed manual and regression test cases for CRM workflows, integrations, and data-driven features.
- Performed data validation to identify missing values, logic errors, and inconsistencies across Salesforce modules.
- Collaborated with developers, product owners, and stakeholders to align testing with business and data requirements.
- Conducted end-user acceptance testing (UAT) to validate usability and performance before production releases.
- Mentored junior QA members and supported quality improvement initiatives to reduce recurring defects.
Reduced production errors with proactive process monitoring and control.
Created detailed reports for management on the status of quality assurance activities.

Education

MSc - Artificial Intelligence & Data Science

University of East London

2024.09 - 2026.04

B.Tech - Electronics & Communication Engineering

MVGR College of Engineering

India

2016.07 - 2020.06

Skills

Programming: Python (basic), SQL
Data & Analytics: Data cleaning, data validation, exploratory data analysis (EDA)
Software Testing: Manual testing, regression testing, test case design, UAT

Platforms & Tools: Salesforce, Jira, MS Excel, Git (basic)
Concepts: Machine Learning fundamentals, statistics, Agile & SDLC

PROJECTS

Exploratory Data Analysis Using Python, - Cleaned and analysed real-world datasets using pandas and numpy., - Handled missing data, outliers, and inconsistencies to improve data quality., - Generated insights using summary statistics and visualisation techniques., Machine Learning Classification Project, - Built and evaluated classification models such as Logistic Regression and Random Forest., - Applied feature selection and performance metrics including accuracy and precision., - Interpreted results to support data-driven decision-making.

Timeline