Data Science Intern

About the Role

As a Data Science Intern at Mico, you will work closely with the team to analyze datasets, identify meaningful patterns, apply statistical techniques, and support the development of data-driven solutions. You will gain hands-on experience in Python programming, exploratory data analysis, data preprocessing, statistical analysis, and machine learning fundamentals while working with real-world datasets.

Key Responsibilities

• Collect, clean, preprocess, and analyze structured datasets using Python.
• Perform exploratory data analysis (EDA) to identify patterns, trends, relationships, and anomalies in data.
• Apply statistical concepts and techniques, including hypothesis testing, p-values, correlation analysis, and regression.
• Build an understanding of linear regression, logistic regression, and other fundamental machine learning algorithms.
• Evaluate regression models using appropriate performance metrics and interpret model results.
• Identify and handle missing values, outliers, categorical variables, and imbalanced datasets.
• Perform feature engineering and prepare numerical, categorical, and Boolean variables for analysis and modelling.
• Write, test, and maintain Python code for data manipulation, analysis, and basic modelling.
• Document analytical findings, explain assumptions, and communicate results to team members.

Requirements & Tech Stack

• Currently pursuing or recently completed a degree in Data Science, Statistics, Mathematics, Computer Science, or a related field.
• Basic to intermediate proficiency in Python programming.
• Familiarity with Python libraries such as Pandas, NumPy, and Scikit-learn.
• Understanding of fundamental statistical concepts, including mean, median, variance, correlation, hypothesis testing, and p-values.
• Knowledge of regression techniques, including simple and multiple linear regression and logistic regression.
• Understanding of model evaluation metrics, including R-squared, Adjusted R-squared, MAE, MSE, and RMSE.
• Familiarity with data cleaning, missing-value treatment, outlier detection, and feature engineering.
• Understanding of categorical and numerical data, Boolean variables, and appropriate data handling techniques.
• Basic knowledge of correlation analysis for numerical and categorical variables.
• Understanding of fundamental machine learning concepts, including overfitting, model validation, and class imbalance.
• Strong analytical thinking, problem-solving skills, and attention to detail.
• Ability to explain analytical approaches, justify data-processing decisions, and communicate findings clearly.

Good to Have

• Experience working on academic projects, personal projects, or internships involving data analysis or machine learning.
• Familiarity with data visualization libraries such as Matplotlib and Seaborn.
• Basic knowledge of SQL and working with structured datasets.
• Exposure to decision trees and other tree-based machine learning algorithms.
• Experience using Jupyter Notebook or Google Colab.