Data Analyst Power Bi

GOUTHAM GUNDAPANENI

Data Analyst

Location: NC Email: ******************@*****.*** Phone: 205-***-**** LinkedIn Summary:

Experienced Business Analyst with expertise in Python, R, SQL, and data visualization using tools like Power BI, Tableau, and Excel. Proven ability to streamline data processes and deliver actionable insights across financial, insurance, and healthcare domains.

Designed and implemented automated data pipelines, handling large-scale datasets (500K to 1M records) for efficient data integration and real-time accessibility across financial, customer, and healthcare datasets.

Developed interactive dashboards in Power BI and Tableau to visualize complex financial and customer data, enhancing decision-making by providing real-time insights into performance metrics, risk factors, and policy trends.

Built predictive models using statistical algorithms and time series analysis with Python (Pandas, NumPy, SciPy) and R to forecast customer churn, patient risk, and portfolio risk factors, enabling targeted strategies and better resource allocation.

Leveraged cloud technologies like AWS (EC2, S3, IAM) and GCP to create scalable cloud-based data processing solutions, improving computation time and supporting efficient management of growing data volumes.

Performed advanced data cleaning and preprocessing using Pandas, NumPy, and SciPy to ensure consistent and high-quality datasets, supporting accurate and reliable analyses across projects.

Collaborated in Agile environments with cross-functional teams to iteratively refine data models, dashboards, and cloud solutions based on business needs and feedback, ensuring the delivery of adaptable and scalable analytics solutions.

Automated A/B testing and data mining to optimize decision-making in investment strategies and patient care protocols, identifying hidden patterns in customer behavior and healthcare outcomes, thus driving informed decision-making and improved performance.

Skills:

Languages: Python, R, SQL

Libraries & Frameworks: NumPy, Pandas, Matplotlib, SciPy Data Visualization Tools: Tableau, Power BI, Excel (Pivot Tables, Power Query) Databases: MySQL, SQL Server, PostgreSQL, MongoDB

Cloud Technologies: AWS (EC2, S3, IAM), GCP

Methodologies: Agile, SDLC

Analytical Skills: Data cleaning, data wrangling, data modeling, data mining, A/B testing, statistical analysis, time series analysis

Operating Systems: Windows, Linux

Experience:

Credit Suisse Aug 2023 - Present

Data Analyst

Designed and implemented an automated data pipeline that collected, cleaned, and processed 1 million data points from multiple financial sources using Python and SQL Server. The pipeline enabled efficient data integration, reducing manual intervention and data processing time significantly.

Developed an interactive Power BI dashboard that visualized complex financial data, including asset performance, risk metrics, and capital allocation. This enabled senior management to make informed investment decisions. The dashboard utilized DAX calculations and Power Query to streamline data representation, providing real-time insights into portfolio performance.

Created and maintained a MySQL centralized database for investment portfolios, improving data retrieval speed by 25% and enhancing forecasting and trend analysis efficiency.

Conducted advanced statistical analysis on portfolio risk factors using Python (Pandas, NumPy, and SciPy), identifying key correlations and trends that led to more robust risk management strategies. This analysis enabled the team to make more informed decisions, mitigating exposure to high-risk assets.

Optimized cloud infrastructure using AWS EC2 and S3, enabling seamless data storage, processing, and retrieval. By leveraging IAM policies, I ensured secure access control, enhancing the confidentiality of sensitive financial data. This infrastructure supported scalable analysis of diverse data sets without compromising system performance.

Automated A/B testing for investment strategies using Python, identifying profitable configurations and increasing strategy success by 10%. Streamlined decision-making processes across multiple teams.

Collaborated with cross-functional teams to provide technical expertise in data management, ensuring the accurate and timely reporting of financial metrics. I spearheaded the Linux-based implementation of statistical models and data processing scripts, allowing the team to execute complex computations with increased efficiency. 7FinCorp Group, India Dec 2020 - Aug 2021

Data Analyst

Engineered a robust data pipeline using R and SQL to automate the extraction, transformation, and loading (ETL) of 500,000+ customer and claims records from disparate data sources. This setup improved data accuracy and availability for analysis by reducing manual intervention.

Applied advanced data wrangling techniques using Pandas and NumPy to clean and preprocess large datasets, improving data quality and enhancing model accuracy by 20%.

Developed predictive models to forecast customer churn and identify high-risk policyholders using statistical algorithms in R. By analyzing historical claim patterns and customer behavior, these models contributed to the development of more personalized retention strategies, resulting in more targeted customer engagement.

Designed and implemented interactive Tableau dashboards to visualize customer trends, retention rates, and policy performance, improving stakeholder decision-making speed by 30% with real-time data insights.

Leveraged GCP to implement scalable cloud solutions, significantly reducing computation time for large-scale data processing tasks. This setup allowed the system to handle growing volumes of data efficiently while maintaining high availability and performance standards.

Collaborated with cross-functional teams using Agile methodologies to iteratively refine the data models and dashboards based on business feedback. This collaboration ensured that the solutions met the evolving needs of marketing and sales teams, allowing for more adaptive policy management strategies.

Conducted detailed data modeling using SQL and statistical techniques to evaluate policy changes across customer demographics, contributing to a 12% increase in profitability through optimized product offerings. Citius Tech, India Sep 2019 - Nov 2020

Data Analyst

Designed and implemented a data processing pipeline using PostgreSQL to collect and store healthcare data from over 800,000 patient records, ensuring real-time accessibility and scalability. This system enabled streamlined data retrieval and improved overall data integrity for analysis.

Conducted data cleaning and preprocessing using Python and SciPy, enhancing dataset accuracy by 25%, leading to more reliable patient predictions and improved analysis outcomes.

Applied time series analysis techniques using SciPy to analyze historical patient data, focusing on identifying trends in disease progression and treatment effectiveness. This insight informed hospital resource allocation and long-term planning efforts.

Built predictive models for patient risk scoring using Matplotlib to visualize the results, which allowed healthcare providers to prioritize critical cases. These models facilitated more effective intervention strategies by forecasting patient deterioration based on vital signs and medical history.

Integrated MongoDB as a secondary database for unstructured data like patient interactions, physician notes, and sensor data, optimizing the storage and analysis of high-volume, diverse datasets. This provided additional context for predictive models and supported more holistic patient care strategies.

Collaborated using Agile methodologies across cross-functional teams of data scientists, clinicians, and software developers to continuously refine the data models and visualizations. This approach ensured that the solutions aligned with clinical needs and were iteratively improved based on feedback.

Automated data mining tasks using Python and SciPy to uncover correlations between treatment protocols and recovery rates, supporting more informed decision-making and improving patient outcomes by 15%. Education:

Master of Science, Computer Science Apr 2023

The University of Alabama at Birmingham, AL

Bachelor of Technology in Information Technology Sep 2020 CVR College of Engineering, India

Contact this candidate