Data Science Intern

Data Science Intern

About Mico

Mico's mission is to empower every brand by building lifetime trust through humanlike technology. By 2030, we aim to be Asia's No.1 Brand Empowerment Company. Mico builds the conversational layer Japanese brands use to reach their customers — LINE, SMS/RCS, Voice AI and web — for more than 5,500 companies across finance, insurance, retail and real estate.

Our four core values guide everything we do:

• Wow the Customer

• Invest in Passion

• Beyond Borders

• Be the Change

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.

Desired Skills & Qualifications

• 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