Hello! I'm Rohitha
I am a driven data science and business analytics master's candidate at the University of North Carolina Charlotte, with a solid foundation in information technology from Jawaharlal Nehru Technological University. Boasting a rich skill set in programming languages and frameworks, I excel in machine learning, AI engineering, and data analysis, demonstrated through impactful work experiences and innovative projects.
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Rohitha Velineni
Data Scientist
+1(980)371-8960
Email:
Address:
Charlotte,NC,USA
Date of Birth:
Sep 30th 2000
Phone:
EXPERIENCE
January 2024 – present
Machine learning and AI engineer
AstraMEP corporation
I have significantly contributed to advancing architectural analysis by leveraging Azure for the automatic detection of architectural elements in floor plans, enhancing both accuracy and efficiency. Expert in using CVAT for image annotation, I've built a comprehensive database for model training. My development of machine learning models via TensorFlow and PyTorch for identifying key structural features has notably improved architectural design processes. Additionally, my work in automating pipeline designs for essential utilities demonstrates a blend of technical prowess and innovative thinking, underpinned by substantial practical experience with cloud platforms like Azure and AWS.
February 2022 – June 2022
Kireeti soft technologies
Data Analyst Intern
I spearheaded the use of Python and SAS to analyze historical payroll data, enabling accurate predictions of clients and liabilities, which led to a 15% increase in payroll revenue and enhanced financial forecasting capabilities. I implemented regression and time series analysis in Python and SAS to forecast payroll clients and liabilities, resulting in a 20% reduction in payroll processing errors and improved cash flow management. Collaborating closely with a senior analyst, I gained practical proficiency in Tableau, Power BI, and SAS tools. Leveraging SAS’s capabilities, I enhanced data accuracy and streamlined processes, leading to a 10% increase in performance.
Feb 2021 - May 2021
Business Analyst Intern
Pronteff IT solutions
I conducted comprehensive data analysis and assessed software project performance based on various operational metrics, resulting in a 20% improvement. I generated and submitted weekly progress reports, presenting findings to the project manager. Utilizing Python for data processing and analysis tasks, I employed C and C++ for low-level system optimizations and performance enhancements to develop robust and scalable software solutions. I translated the insights gained into impactful data visualizations using Tableau and also implemented interactive data visualizations through Java applets, aiding the team in understanding software market dynamics. I developed a profound understanding of software market fluctuations, enabling informed strategic decisions. These decisions, supported by detailed analyses using Python, C, and C++, contributed significantly to a 16% increase in overall project performance.
EDUCATION
August 2022 – May 2024
University of North Carolina Charlotte
Masters in Data Science and business analytics
August 2018 – May 2022
Jawaharlal Nehru Institute of Technological University kakinada
Bachelor of Technology in Information Technology
ACCOMPLISHMENTS
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Presented an IEEE paper on “Analyzing crop yield using Machine learning”
SKILLS
Python, AWS, Java, R, C, C++
HTML, CSS
SQL,MongoDB
Machine learning, TensorFlow, PyTorch, Pandas, Scipy, Sci-kit learn.
Tableau, Powerbi
MS office, Android Studio, Github, Canva
PROJECTS
Prediction of diabetes using Machine Learning
NBA fantasy creation using snowflake
I devised an innovative approach named Random Forest Optimization using the Backward technique in Python. Through the utilization of heat maps, I reduced attribute complexity by 10%, streamlining user interaction by 12%. I identified and retained key attributes influencing outcomes, employing a strategic backward elimination technique. This process significantly enhanced the model's efficiency and interpretability.
I sought to empower basketball aficionados by tactically curating fantasy teams, and harnessing an array of player-centric data including scoring analysis, three-pointer proficiency, free throw rates, and win-loss ratios. I implemented Snowflake's formidable querying prowess to compute comprehensive player scores, amalgamating vital metrics like points, rebounds, assists, and defensive contributions. This approach was instrumental in identifying standout players, facilitating fans in assembling fantasy teams that showcased remarkable on-court prowess. This initiative led to a 60% increase in user engagement by delivering an enriched fantasy team selection experience.
Coffee Chain Analysis using Tableau and Streamlit
I improved sales tactics by strategically identifying high-performing coffee products and pinpointing areas requiring sales uplift. By harnessing Tableau's robust visualization tools, I unveiled insightful data patterns and correlations. I distinguished top-selling coffee items, which drove 70% of total sales. These findings were then translated into a Streamlit application, enabling real-time monitoring and informed decision-making, thus enhancing the effectiveness of sales strategies and operational efficiency.