Mathematics and Computer Science graduate with a specialization in Statistics. I focus on data analysis, statistical modeling, research support, and AI data annotation, using structured thinking to turn data into clear and useful insights.
Statistics Specialist | Data Analysis | Research Support
I have an academic foundation in Mathematics and Computer Science, with specialized training in Statistics. My work combines analytical reasoning, quantitative methods, and practical computing skills to solve data-related problems. I am interested in opportunities involving data analysis, statistical interpretation, AI data annotation, and research work that requires accuracy, consistency, and critical thinking.
Specialization: Statistics
Applying statistical methods to summarize data, test hypotheses, and support decision-making.
Cleaning, exploring, and interpreting datasets to identify patterns, trends, and insights.
Using probability and modeling techniques to understand relationships within structured data.
Using Python libraries for analysis, preprocessing, and visualization of data.
Creating summaries, charts, and reports that communicate findings clearly.
Writing simple queries to retrieve, filter, and organize data efficiently.
NumPy, Pandas, Matplotlib
Basic statistical computing and analysis
Data organization, formulas, charts, and summaries
Basic querying for structured data tasks
Interactive analysis and reporting environment
Clear charting and insight presentation
Some of my recent work in data analysis and statistical modeling.
Performed exploratory data analysis on a public dataset to identify trends, central tendencies, and variable relationships using summary statistics and visual interpretation.
Built a regression model to estimate outcomes from structured data and evaluated performance using suitable metrics to understand predictive accuracy and reliability.
Designed a simple dashboard to present trends and findings in a visual format that supports quick understanding and better communication of results.
Worked on data labeling, response evaluation, and quality-checking tasks that required attention to detail, consistency, and adherence to structured guidelines.
Completed self-directed projects involving dataset cleaning, descriptive analysis, basic modeling, and visual presentation of findings to strengthen practical statistical skills.
Applied mathematical and statistical reasoning to interpret data, summarize observations, and contribute to structured research-oriented tasks.
Strong ability to break down problems and evaluate them logically.
Comfortable identifying patterns and explaining findings clearly.
Applies quantitative thinking to real-world data situations.
Maintains accuracy, consistency, and structure in technical work.