| | | 20,000+ Fresh Resumes Monthly | |
|
|
| |
Click here or scroll down to respond to this candidateCandidate's Name
United States PHONE NUMBER AVAILABLE EMAIL AVAILABLE LinkedIn GitHubEDUCATION 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 SKILLSSkills: 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. EXPERIENCEYiddish 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 Markovs 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 & PROJECTSAwarded High Impact Grant for Cybersecurity Research Analysis Advanced Financial ForecastingDevised 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 AnalysisFormulated 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. |
