Data Analyst Business Intelligence

KRISHNA KRUPA SINGAMSHETTY

+1-330-***-**** ***********@*****.*** linkedin Github

Summary

Results-driven data analyst with expertise in transforming complex datasets into actionable insights to support business decision-making. Skilled in statistical analysis, predictive modeling, and data visualization to drive operational efficiency and strategic growth. Experienced in developing interactive dashboards, optimizing data workflows, and collaborating with cross- functional teams to improve business intelligence. Holds a Master’s in Business Analytics with a strong foundation in analytical problem-solving and a passion for leveraging data to solve real-world challenges.

Skills

•Data Analysis

•Data Cleaning

•Project Management

•SQL Management

•Business Intelligence

•Statistical Forecasting

•Predictive Modeling

•Process Optimization

•Cross-Functional Collaboration

Technical Skills

•Programming Languages: Python (Matplotlib, Pandas, Numpy), SQL, R

•Databases: MySQL, MongoDB

•Data Visualization Tools: Tableau, Power BI, Power Query

•Analytical Tools: Microsoft SQL Server, Advanced Excel (Lookups, Pivot tables), MS Office

•Methodologies: Agile, Scrum

•Operating Systems: Windows

•Statistical Methods: Predictive Modeling, Trend Analysis

Experience

Data Analyst Intern 07/2024 to 12/2024

Springer Capital Chicago, IL

•Collaborated with 5+ departments to gather data requirements, ensuring alignment with business needs and improving workflow efficiency by 25%.

•Performed data cleansing and transformation on datasets exceeding 100K records, enhancing data accuracy by 98% for reporting.

•Designed and developed 10+ interactive dashboards in Power BI, leading to a 40% improvement in KPI tracking and business metric insights.

•Automated data retrieval processes using Power Automate, reducing manual workload by 50% and increasing reporting efficiency.

•Validated and tested data workflows, ensuring 99% consistency and accuracy in analysis and reporting.

•Optimized reporting processes, reducing report generation time by 30% and improving accessibility for stakeholders.

Associate Analyst 08/2022 to 07/2023

R1RCM Hyderabad, India

•Analyzed provider-side claim data, processing over 200K records monthly to ensure billing accuracy and compliance.

•Utilized SQL and reporting tools to track provider performance, leading to a 20% reduction in claim errors and delays.

•Developed 15+ automated reports and dashboards, improving financial tracking and provider performance evaluation.

•Assisted in resolving 500+ complex claim issues, expediting payments and improving turnaround time by 35%.

Data Visualization & Analytics

Project

•Designed interactive dashboards in Power BI and Tableau, visualizing key metrics such as sales trends, revenue performance, and customer behavior.

•Optimized raw data using Python (Pandas, NumPy) and SQL, enhancing data accuracy and improving decision-making efficiency by 20%.

•Delivered real-time insights through dynamic visualizations, enabling executives and stakeholders to make informed, data-driven business decisions.

Customer Segmentation & Sales Analysis

•Examined and visualized sales data using Python (Pandas, Matplotlib), SQL, and Power BI to uncover customer behavior trends and purchasing patterns.

•Segmented customers based on purchasing habits, enabling targeted marketing strategies that boosted quarterly revenue by 15%.

•Implemented statistical forecasting models to predict sales trends, optimizing inventory management and reducing overstock costs.

Data Analytics & Predictive Modeling in Online Retail

•Applied R (dplyr, ggplot2) for data wrangling and customer behavior analysis, increasing insights accuracy by 20%.

•Performed descriptive statistics and missing data analysis, enhancing dataset completeness by 15%.

•Analyzed seasonal sales trends using advanced date and time functions, improving retail sales forecasting and inventory planning.

Image Classification: Cats & Dogs Using Convolutional Neural Networks

•Developed a convolutional neural network (CNN) in Python using TensorFlow, achieving 95% classification accuracy through transfer learning and hyperparameter tuning.

•Enhanced model robustness by 20% using data augmentation techniques such as rotation, flipping, and scaling.

•Optimized deep learning pipelines, reducing training time by 30% while maintaining model performance.

Education

Master of Science: Business Analytics 12/2024

Kent State University

BBA: Business Administration, Human Resources 07/2022

Osmania University

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