Data Scientist

Technical Skills:

• Languages: Python - (Numpy, Pandas), Wolfram - Mathematica
• Cloud Services: AWS
• Developer Tools and Databases: Docker, Git, Jupyter Notebooks, Visual Studio, MongoDB, SQL
• Machine Learning/AI: Tensorflow, Keras, PyTorch, scikit-learn, Deep Learning
• Visualization Tools: Tableau, PowerBI, Python - Seaborn, Matplotlib
• Data Science Concepts: A/B testing, ETL, Data science pipeline (cleansing, wrangling, interpretation, modeling)
• Statistics, Time series, Experimental design, Hypothesis testing, APIs, Excel

Education

• M.Sc., Data Science

  University of Aberdeen (November 2023)

• B.Tech., Mechanical Engineering

  JNTU - Kakinada (November 2017)

Work Experience

Research Assistant @ TOSSIB @ University of Aberdeen (November 2022 - October 2023)

• Advanced-Data Analysis: Applied cluster analysis, K-Nearest Neighbors (KNN) to improve sanitation, achieving an 82% improvement in prediction accuracy.
• Sanitation Index Development: Successfully created the Sanitation Index and integrated it into government planning, resulting in a significant 14% improvement in development goals.
• Collaborative Research: Worked closely with a diverse team of experts in data science and environmental engineering from the University of Manchester and Brazil to gain valuable research insights.
• Publication and Collaboration: Co-authored a research paper highlighting the sanitation index of Brazil, contributing to the field’s knowledge. Collaborated efficiently with team members using Git for version control and task assignment.

Transportation Specialist @Amazon Development Center India (August 2020 – May 2022)

• Data Optimization: Utilized Python and SQL to manipulate, analyze, and visualize large data sets, improving transportation operations.
• Reporting and Automation: Generated reports and automated processes, saving 41 man-hours per week, and facilitating data-driven decision-making.
• Operational Efficiency: Enhanced logistics efficiency by 19% and strategic planning through data-backed strategies and seamless collaboration with cross-functional teams.
• Leadership and Collaboration: Led and mentored a team of transportation associates while aligning transportation strategies with business requirements.

Projects

Prediction of Electricity in UK quantifying Renewable sources

• Led a machine learning project that improved electricity price predictions by 18% through the development of predictive models using Regression, Time Series analysis, and neural networks achieving a 12% enhancement in accuracy while considering demand, supply, and renewable energy factors.
• Conducted extensive data analysis, revealing correlations between electricity prices and renewable energy integration.
• Demonstrated how renewable energies reduced price volatility by 27% and decreased reliance on traditional energy.

A/B Testing Project: Cookie Cats Game

Objective: Investigated the impact of gate placement on player retention in Cookie Cats, leveraging A/B testing methodologies.

• Conducted rigorous A/B testing experiments with gates positioned at levels 30 and 40.
• Identified a significant increase in 7-day retention rates when the gate was placed at level 30.
• Explored the concept of hedonic adaptation and its unexpected influence on player engagement.
• Analyzed additional metrics including game rounds played and in-game purchases to gain comprehensive insights.
• Fostered community engagement and discussions surrounding A/B testing methodologies and user experience design.
• Maintained project transparency and collaboration through a dedicated GitHub repository.

Outcome: Contributed to a deeper understanding of user behavior in gaming environments and provided actionable insights for enhancing player experiences in Cookie Cats.