Data Analyst

Technical Skills

Python, SQL, Tableau, Power BI, AWS (Lambda, Redshift, S3), Snowflake, Hadoop, Spark, Scikit-learn, TensorFlow

Education

• Master of Science in Data Science | University of North Texas, Denton, Texas, USA
• Bachelor of Technology in Computer Science and Engineering | Jawaharlal Nehru Technological University, India

Work Experience

Data Analyst @ Humana (August 2023 - Present)

• Conducted comprehensive analysis of healthcare datasets using SQL and Python, reducing data processing time by 20%.
• Developed and optimized ETL workflows with AWS Lambda, increasing pipeline efficiency by 25%.
• Created custom Power BI dashboards, enhancing decision-making efficiency by 25%.
• Built predictive models using Python (scikit-learn), improving patient admission forecasting accuracy.
• Implemented A/B testing frameworks, boosting optimization initiative effectiveness by 30%.

Data Analyst @ Capgemini (January 2020 - July 2022)

• Analyzed supply chain data using SQL and Power BI, reducing process inefficiencies by 20%.
• Designed ETL pipelines with Informatica and Snowflake, cutting data processing times by 30%.
• Created Tableau dashboards for real-time KPI visualization, increasing operational efficiency by 70%.
• Automated MS Excel reports with Python, saving 10 hours per week.

Projects

Optimizing Healthcare Data Processing

Tools: SQL, Python, AWS Lambda, Power BI
Objective: Streamline healthcare data workflows and enhance reporting efficiency.
Description: Automated ETL processes and developed dashboards for patient admission predictions, reducing manual tasks and improving decision-making. Impact: Reduced data processing time by 20% and boosted data accuracy by 25%.

Supply Chain Performance Dashboard

Tools: Tableau, SQL, Python
Objective: Visualize and optimize supply chain KPIs across multiple nodes.
Description: Designed interactive dashboards to track inventory levels, delivery times, and bottlenecks. Impact: Increased operational efficiency by 70%.

Predictive Modeling for Loan Default Rates

Tools: Python (Scikit-learn), Snowflake
Objective: Build machine learning models for credit risk prediction.
Description: Developed regression models and evaluated performance metrics to forecast loan defaults. Impact: Enhanced prediction accuracy, reducing financial risk.

Contact Information

• Email: nainieshwitha11@gmail.com
• LinkedIn: LinkedIn Profile
• GitHub: GitHub Profile (Add link)

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