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Data Science with less than a year in Machine Learning & Statistical Analysis.
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Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
A highly motivated Computer Science and Engineering graduate with hands-on experience in data science, machine learning, and statistical analysis. Passionate about transforming complex datasets into actionable insights. Seeking a Data Science role where I can apply my technical skills in Python, ML algorithms, and data visualisation to solve real-world business challenges and drive data-driven decision making.
PSN Engineering College, Tirunelveli
Bachelor of Engineering · Computer Science & Engineering
August 1, 2022 – June 30, 2026
Don Bosco Matriculation Higher Secondary School
Higher Secondary Certificate (HSC) · 12th Grade
June 1, 2021 – May 31, 2022
Don Bosco Matriculation Higher Secondary School
Secondary School Leaving Certificate (SSLC) · 10th Grade
June 1, 2019 – May 31, 2020
Corizo Edutech
Data Science Intern
March 1, 2026 – May 31, 2026
India
Body Measurement Analysis — NHANES 2020
January 1, 2026 – Present
Analysed NHANES 2020 body measurement data using multidimensional NumPy matrix operations across gender groups. Computed BMI, WHtR, and WHR as derived features; performed statistical analysis (mean, median, skewness, kurtosis) and Pearson & Spearman correlations. Generated histograms, box-and-whisker plots, and scatterplot matrices to visualise sex-based differences in body composition.
Semiconductor Yield Prediction — Manufacturing Quality ML
January 1, 2026 – Present
Built a binary classification pipeline on a 1,567 × 591 semiconductor sensor dataset to predict wafer yield (Pass/Fail). Performed comprehensive data cleansing: removed zero-variance features, dropped columns with >50% missing values, and applied median imputation. Applied PCA for dimensionality reduction and SMOTE to counter severe class imbalance in the target variable. Tuned classifiers using GridSearchCV + Stratified K-Fold CV, optimising for precision, recall, and ROC-AUC.
Fraud Detection Using Machine Learning
January 1, 2025 – January 1, 2026
Built an end-to-end fraud detection system on the PaySim synthetic financial dataset simulating real-world mobile money transactions. Addressed severe class imbalance using SMOTE oversampling, significantly improving minority-class detection performance. Benchmarked multiple classifiers (Logistic Regression, Random Forest, XGBoost) on precision, recall, and F1-score. Achieved high recall on fraudulent transactions, minimising false negatives critical for financial risk management.
Data Science Training Certificate
Corizo Edutech
May 1, 2026 – Present
Cultural Fit Analysis
The candidate's projects demonstrate a strong interest in applying data science to diverse domains, from finance (fraud detection) to healthcare (body measurement analysis) and manufacturing (semiconductor yield prediction). This breadth of application, even within academic/internship contexts, suggests adaptability and a willingness to tackle varied challenges. The internship at Corizo Edutech, certified by multiple bodies, indicates a commitment to structured learning and professional development. However, the experience is limited to internships and academic projects, which might require more mentorship in a fast-paced industry setting.
Soft Skills & Operational Fit
The candidate highlights analytical thinking, problem-solving, attention to detail, team collaboration, and continuous learning as soft skills. These align well with the demands of a Data Science role, indicating a proactive and collaborative approach to work. The project descriptions further support these claims, particularly in the detailed steps taken for data cleansing and model optimization.