Sai Abhilash Bojja

551-***-****

OBJECTIVE

A highly motivated and detail-oriented Data Analyst with a strong background in data analytics and statistical analysis, seeking to leverage my analytical skills and passion for working with data to contribute to a dynamic organization's data-driven decision-making process. SKILLS

Data Analysis: Proficient in data acquisition, cleansing, and manipulation using PySpark, Python, R, and advanced SQL. Analytics: Experienced in applying statistical analysis, machine learning, and data mining techniques to draw insights from complex data sets.(SAS, SPSS)

Tools: Familiar with Databricks, Snowflake, and Google Big Query for scalable data analysis. Data Visualization: Skilled in using data exploration and visualization tools to present insights effectively.( Tableau, PowerBI, Excel) Communication: Strong communication skills to distill data-driven insights into compelling stories for various audiences. Time Management: Proven ability to work in a fast-paced environment, handling multiple priorities simultaneously. Passion: Deep passion for the music and entertainment industry, with familiarity in music streaming services and social media platforms. EXPERIENCE

COGNIZANT August 2021 – July 2022

Data Analyst Intern Hyderabad, India

-Assisted in acquiring, organizing, and cleansing complex data from multiple sources using tools like PySpark and advanced SQL.

-Collaborated with the Data & Analytics team to develop automated and scalable data analysis solutions using modern big data architectures, including Databricks and Snowflake.

-Applied supervised and unsupervised learning techniques to identify patterns in user behavior and uncover valuable insights in behavioral data.

-Conducted campaign, product, content, and audience analysis to support company priorities and reported on key metrics to measure success.

-Collaborated with Engineering and Product teams to contribute to data projects from solution discovery to QA. ACCENTURE February 2020 –June 2021

Analyst Trainee(internship) Hyderabad, India

-Worked with the data team to apply supervised and unsupervised learning techniques, such as linear and logistic regression, decision trees, and clustering to derive insights and quantify conclusions from data.

-Documented data requirements, rules, and assumptions for various projects, ensuring data integrity and consistency.

-Conducted data mining in distributed systems, including Databricks, Google Big Query, and Snowflake, to manipulate large transaction-level datasets and interpret data trends from multiple sources. PROJECTS

Social Network Analysis

• Analyzed a Facebook network dataset provided by Stanford University, consisting of 10,000 nodes and 50,000 edges

• Took advantage of NetworkX Python package to calculate essential centralities, such as Degree, Closeness, and Betweenness centrality, to assess the network's structure and influence

• Identified nodes with the highest Degree centrality, representing the most connected individuals in the network

• Computed Closeness centrality to measure the average distance of each node to all other nodes, and Betweenness centrality to identify critical nodes that act as bridges between different groups within the network

• Produced insightful visualizations using Tableau, presenting trends and patterns within the Facebook network

• Fabricated interactive dashboards showcasing key metrics and influential nodes for a user-friendly data exploration experience Equity Portfolio Management

• Managed a virtual $5 million equity portfolio comprising ten high-tech stocks using R for data analysis and automation

• Defined and implemented a "5 days rebalancing of buying low" trading strategy, identifying stocks with the most significant percentage price drops to maximize returns

• Designed a dividend calculation mechanism, accounting for dividend payouts on specific dates for stocks in the portfolio

• Generated MTM curves, dividend income reports, and trading strategy effectiveness visualizations to provide insights into portfolio performance

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• Proficiency in data analysis, financial data management, trading strategy development, and automation with R, showcasing expertise in equity portfolio management

Loan Default Prediction

• Developed and compared predictive models using various algorithms, including logistic regression, random forests, gradient boosting, and support vector machines, to predict credit card defaults. Demonstrated proficiency in machine learning techniques

• Collected, cleaned, and pre-processed a diverse dataset comprising customer demographics, purchasing habits, credit history, and payment history. Employed data normalization, feature scaling, and one-hot encoding to prepare data for analysis

• Evaluated model performance using essential metrics such as accuracy, precision, recall, and F1-score. Identified gradient boosting as the top-performing algorithm, achieving an accuracy rate of 80.72%, highlighting a data-driven approach to solving complex problems Additional qualifications

• Strong problem-solving skills and ability to apply mathematical and statistical concepts to real-world challenges.

• Excellent communication and teamwork skills gained through collaboration with cross-functional teams

• Quick learner with a strong desire to expand knowledge and skills in data analysis and machine learning. Collected, cleaned, and pre- processed a diverse dataset comprising customer demographics, purchasing habits, credit history, and payment history. Employed data normalization, feature scaling, and one-hot encoding to prepare data for analysis. EDUCATION

New Jersey Institute of Technology, Newark Sept 2022 –Dec 2023 Master in Science (MS) Data Science GPA: 3.4/4.0 New Jersey, NJ

Vasavi College of Engineering July 2018 – June 2022 Bachelor of Technology (B.Tech) Civil Engineering GPA: 3.2/4.0 Hyderabad, India RELEVANT COURSEWORK

Statistical Analysis

Data Mining

Machine Learning

Advanced SQL

Data Visualization

Scripting with Python and R

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