Motivated and detail-oriented professional with experience in AI training, data annotation, red teaming, and reward learning for large language models. Skilled in ensuring accuracy, quality, and safety in AI outputs thro…
Motivated and detail-oriented professional with experience in AI training, data annotation, red teaming, and reward learning for large language models. Skilled in ensuring accuracy, quality, and safety in AI outputs through testing and evaluation. Recently completed intensive training in data science, gaining hands-on experience in Python, SQL, Power BI, data analysis, visualization, and machine learning. Strong background in working within remote, flexible environments, demonstrating adaptability, initiative, and collaboration with diverse teams. Currently pursuing opportunities in data science and analytics, with a focus on applying statistical modeling, exploratory data analysis, and predictive modeling to solve real-world problems.
Trained and evaluated NLP models, improving data annotation quality and AI performance. Adapted to changing guidelines and tools in a fast-paced remote environment. Collaborated with cross-functional teams on AI safety and reliability initiatives
Power BI – Data Professionals Survey Dashboard Analyzed a global survey of data professionals to uncover insights on salaries, career challenges, occupations, and work-life balance. Cleaned and transformed the dataset in Power BI (splitting columns, creating calculated fields, handling missing values) before building an interactive dashboard. Delivered visualizations highlighting salary distribution by country/role, entry challenges into the data field, occupational breakdowns, and work-life balance trends.
Analyzed 2,300+ records of company layoffs across industries and countries. Performed data cleaning (removing duplicates, handling nulls, standardizing values) and transformations in MySQL. Explored trends in layoffs by industry, geography, company stage, funding relationships, and time distribution.
Built an end-to-end regression pipeline to predict insurance losses using structured data (188K+ records). Applied data cleaning, outlier treatment, and categorical encoding. Engineered features by removing low-variance, correlated, and redundant variables. Trained and tuned models including Random Forest, achieving improved RMSE through hyperparameter optimization.
Regression Project Built an end-to-end machine learning regression pipeline, including data cleaning, feature engineering, model development, and validation. Implemented and compared multiple models (Linear, Ridge, Lasso, ElasticNet, Random Forest, XGBoost, KNN, SVR, MLP) using metrics such as MSE and R², with hyperparameter tuning through grid search and cross-validation.
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