Meghana Varghese

Conducted mathematical data science assignments, including linear algebra, probability, and differential equations. Developed proficiency in Python programming and statistical analysis. Implemented machine learning algorithms such as regression, correlation, gradient descent, and PCA. Gained hands-on experience with neural networks, including CNN, and medical image processing. Collaborated with leading scientists on industry-relevant projects and programming assignments. Actively involved in data visualization using python and participated in mentorship sessions by senior scientists.

Assessing and suggesting new technologies to improve data analytics performance and maintain market competitiveness. Examining market patterns spearheaded the use of data to analyze consumer behavior, market trends, and competitive environments. By using these insights to inform strategic choices, the company was able to increase revenue by 20% and grow its client base by 15%. Actively involved in stakeholder setting and tracking KPIs and KRIs for business development efforts while keeping an eye on continuous improvement. Engaging with people, managing a group of financial consultants, and working together to increase the company's profitability.

Analyzed the global terrorist dataset and derived insights from it to recognize attack types by region in particular years, thus understanding terrorist attack patterns for counter-terrorism policies. A study on the prediction of accurate breast cancer using logistic regression and classifying new input categories into benign and malignant tumors. Developed an effective email spam classifier using ML and NLP that can automatically identify and filter spam emails.

Designed and developed business intelligence solutions, dashboards, and reports mainly using Microsoft Power BI, resulting in improved data visualization and insights for business stakeholders. Built data models and implemented data transformations using Power Query Editor, resulting in efficient and optimized data retrieval and analysis. Created and managed data refresh schedules and maintained data lifecycle management, ensuring dashboards were up-to-date and accurate. Participated in stakeholder engagement, managed inventory, prepared financial reports, developed KPIs and data for management and stakeholders, and coordinated activities among inventory management, sales, production, and logistics teams.