Thulasi Prasad Veerapalli

Responsibilities:

• Streamlined data processing for faster results by implementing advanced analytics tools.
• Locate important data sources and streamline process of gathering data and Selecting features, building classifiers, and optimising them with use of machine learning tools
• Preprocess both structured and unstructured data
• Engage in data mining, or process of removing useful information from important data sources
• Experienced in machine learning algorithms, ensemble modeling, and data visualization for impactful insights
• Skilled in devising strategic solutions for company challenges, collaborating effectively across product and engineering teams
• Improving data gathering techniques to incorporate all pertinent data for creating analytical systems
• Effective data management, organization, and manipulation are all part of handling data
• Eliminating mistakes, inconsistencies, and outliers is process of purifying data
• Verifying data integrity and comparing it to other sources are necessary steps in confirming correctness
• Examine great deal of data to find patterns and trends
• Designed, developed, and implemented robust Extract-Transform-Load (ETL) processes to efficiently extract data from diverse sources including relational databases, APIs, flat files, and cloud-based storage
• Leveraged industry standard ETL tools such as Apache Spark, Talend, and Informatica to transform raw data into structured formats suitable for analytics and reporting purposes
• Collaborated with cross-functional teams to ensure data integrity and accuracy, resulting in better-informed decisions
• Developed custom algorithms to optimize data mining, increasing the effectiveness of analytical insights.

• Assisting in gathering data from various sources such as databases, files, APIs, and web scraping
• Creating basic visualizations (such as plots, charts, and graphs) to present data insights using tools like Excel, matplotlib, or seaborn.
• Contributing to the preparation of reports summarizing analysis findings, key insights, and actionable recommendations for stakeholders.
• Collaborating with cross-functional teams, including data scientists, engineers, and business analysts, to achieve project goals and deliver actionable insights.
• Enhanced data visualization capabilities for better decision-making with the use of advanced BI tools.
• Streamlined data collection processes by implementing efficient data mining techniques.