Data Analyst Analytics

Rahul Kumar Shivakumar

United States 571-***-**** ********@***.*** LinkedIn GitHub

EDUCATION

● George Mason University, MS Data Analytics Engineering Coursework: Statistical Analysis, Big Data, Financial Engineering, DBMS, Applied ML, Predictive Analytics.

● Visvesvaraya Technology University, Bachelor of Engineering SKILLS

Skills: Supervised & Unsupervised Learning, Hypothesis Testing, Deep Learning, Problem solving, Data Visualization, Pipelining, Predictive Forecasting, A/B Testing, Ad-hoc Analysis, Product Analytics, Database designing, CI/CD. Tools: Python, R, SQL, Tableau, Matplotlib, Seaborn, Postgres, Databricks, Snowflake, Hadoop, Spark, VBA, Scikit-learn, TensorFlow, Keras, Pytorch, OpenCV, NLTK, BERT, AWS, Azure, Salesforce, Kafka, Docker. EXPERIENCE

Yiddish Arts and Academics Association of North America – Data Analyst Apprentice 06/2024 – Present

● Collect, clean, and select relevant data and variables to perform an analytical summary of the main website's performance in 2023, focusing on metrics such as total views and regional visitation patterns.

● Developed a predictive model framework using R program to estimate the website's performance for 2024, present the findings along with SEO and marketing strategies to address vulnerabilities and capitalize on opportunities. George Mason University – Research Analyst 08/2023 – 05/2024

● Performed risk assessment to identify industry-specific threats faced by Industries using LLMs by mapping the Verizon (DBIR) database, MITRE Atlas Database and Census Dataset, providing a probabilistic analysis of impact.

● Employed MongoDB and ETL on large JSON files, converting unstructured data into structured data enabling streamlined analysis process. Constructed a custom Git API to automate the retrieval of data from Git.

● Curated Neural Networks (GAN) to augment data for realistic data diversity enhancing the risk model by 20%, leveraged Lorenz curve and Markov’s rule to assess the risk distribution and established risk framework. Sansera Engineering – Data Analyst 07/2021 – 07/2022

● Fostered cross-departmental collaboration (Manufacture, New Product Development and Forging units) to improve manufacturing quality, using Power BI visualizations to enhance KPIs and assist in the decision making.

● Spearheaded operations research using R to perform t-tests, optimizing machine calibration by varying machine performances. This strategic analysis achieved a 16% reduction in manufacturing time on shop floor bottlenecks.

● Achieved a 0.5 Sigma level improvement in defect rates, as measured by Six Sigma Metrics and statistical analysis of production data before and after implementing visualizations to strengthen B2B services. ACHIEVEMENTS & PROJECTS

Awarded High Impact Grant for Cybersecurity Research Analysis Advanced Financial Forecasting

Devised an LSTM-based model using Keras and TensorFlow to forecast Tesla (TSLA) stock prices, implemented a cutting-edge approach by segmenting historical data into 21-month cycles. Created visualizations and collaborated with app developers to integrate the model into an app, transforming it into a real-world application. E-commerce Customer Cohort Analysis

Formulated KPIs like recency, frequency and monetary scores and performed RFM Analysis to customers upon studying their purchase behavior. Leveraging unsupervised learning techniques k-means clustering, created six customer segments on these metrics and devised targeted strategies for high-performing and at-risk customer segments informed by A/B Testing and market basket analysis using Naive Bayes and Apriori Algorithm. Multilingual Tweet Intimacy Analysis (NLP)

Guided a multilingual intimacy prediction project by fine-tuning BERT (Multilingual) and XLM-RoBERTa models trained on 6 languages to accurately predict intimacy in tweets across 10 languages. Enhanced model accuracy with machine learning techniques such as Regression and SVR, achieving a Pearson correlation of 0.85. Evaluated performance using MSE, delivering actionable insights that informed strategic content moderation and user engagement strategies across diverse linguistic audiences.

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