Data Science Intern

About this role

About the role: - You'll support our data science initiatives by building, refining, and analyzing machine learning models under mentor guidance - Work across the data lifecycle: from exploring raw datasets through model deployment and performance evaluation - Contribute to predictive analytics projects that inform business decisions and drive product improvements What you'll work on: - Data preparation including cleaning, handling missing values, detecting outliers, and engineering new features - Building supervised learning models using linear regression, logistic regression, and tree-based approaches - Evaluating model performance using metrics such as R-squared, Adjusted R-squared, MAE, MSE, and RMSE - Analyzing relationships between variables through correlation studies for both numerical and categorical data - Identifying and mitigating common challenges like overfitting and class imbalance in datasets - Documenting your methods and presenting insights to stakeholders in clear, understandable language What you'll need: - Currently pursuing or recently completed a degree in Data Science, Statistics, Mathematics, Computer Science, or a related field - Basic to intermediate proficiency in Python - Working knowledge of Pandas, NumPy, and Scikit-learn libraries - Solid grasp of statistical foundations: mean, median, variance, correlation, hypothesis testing, and p-values - Understanding of regression techniques (simple, multiple, and logistic) and their applications - Experience distinguishing between and handling categorical and numerical data types - Comfortable with Boolean variables and appropriate transformation methods - Strong logical reasoning, ability to solve complex problems, and meticulous attention to detail - Capacity to articulate your analytical methods and convey results to both technical and non-technical audiences It helps if you have: - Academic, personal, or prior internship projects demonstrating data analysis or machine learning work - Familiarity with visualization libraries such as Matplotlib and Seaborn - Basic SQL skills and exposure to structured datasets - Knowledge of decision tree algorithms and other tree-based ensemble methods - Experience working in Jupyter Notebook or Google Colab environments