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Data Science with less than a year in data analytics and machine learning.
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Assessing your cultural and operational fit
Final year BSc IT student with 3 months of industry training at Acmegrade in data analytics and data science. Analyzed 10,000+ real-world records, built ML forecasting pipelines achieving 88% accuracy, and delivered interactive dashboards for decision-making. Skilled in Python, SQL, Power BI, and machine learning. Detail-oriented learner passionate about turning raw data into actionable insights.
Viva College
Bachelor of Science · Information Technology
August 1, 2022 – June 30, 2025
Acmegrade
Data Science Trainee
January 1, 2026 – March 1, 2026
India
PJR Car Price Prediction
April 1, 2025 – June 1, 2025
Modeled prices on 5,000+ vehicle records with 20+ features including make, mileage, and fuel type. Achieved 88% accuracy (R2=0.88) with CatBoost, outperforming Random Forest (85%). Improved performance by 8% via feature selection and log-transformation of skewed targets.
PJR Sales Forecasting
January 1, 2025 – March 1, 2025
Analyzed 10,000+ sales records performing time-series EDA and feature engineering. Improved forecast accuracy by 15% using XGBoost & LightGBM with cross-validation. Reduced RMSE by 12% via lag features, rolling stats, and target encoding.
Cultural Fit Analysis
The candidate's academic projects and internship demonstrate a strong interest and foundational skill set in data science. The projects cover different types of problems (sales forecasting, car price prediction) and utilize a variety of ML algorithms, indicating a breadth of exposure. The internship at Acmegrade further solidifies practical application of skills. This aligns well with a data science role that values continuous learning and practical problem-solving.
Soft Skills & Operational Fit
The candidate's resume highlights a detail-oriented learning approach and a passion for turning raw data into actionable insights. The internship experience suggests an ability to work with real-world datasets and contribute to decision-making through data analysis and visualization. The focus on building reusable pipelines indicates an understanding of efficiency and best practices in data processing.