Data Scientist Intern (6 Months)

Responsibilites

• Collect, clean, and organize data from different sources.
• Perform exploratory data analysis to identify trends, patterns, and data quality issues.
• Write basic SQL queries to extract and transform data for analysis.
• Prepare datasets for dashboards, reports, and machine learning experiments.
• Help build and evaluate simple statistical or machine learning models under guidance.
• Create clear charts, summaries, and documentation to communicate findings.
• Automate repetitive data preparation or reporting tasks using Python where appropriate.
• Document assumptions, data definitions, and analysis steps for review and reuse.

Requirement

• Currently pursuing a degree or diploma in Data Science, Computer Science, Statistics, Mathematics, Engineering, Business Analytics, or a related field.
• Basic knowledge of Python and SQL, with willingness to apply them in a business setting.
• Familiarity with data cleaning, exploratory analysis, and basic statistics.
• Comfortable working with spreadsheets or structured datasets.
• Good attention to detail when checking data accuracy and assumptions.
• Able to explain findings clearly and ask questions when requirements or data are unclear.
• Curious, self-motivated, and keen to learn from feedback.

Preferred Skills

• Exposure to Python libraries such as pandas, NumPy, scikit-learn, matplotlib, or similar tools.
• Basic understanding for MongoDB and PostgreSql.
• Basic understanding of machine learning concepts such as regression, classification, clustering, or model evaluation.
• Familiarity with data visualization tools such as Power BI, Tableau, or Python visualization libraries. Mostly will use Python to generate the dashboard.
• Basic knowledge of Git or version control.
• Interest in applying data science to business, customer, network, operations, or digital use cases.

What You'll Gain

• Hands-on exposure to real-world data science and analytics projects.
• Practical experience using Python and SQL in a professional environment.
• Experience working with data quality checks, exploratory analysis, and business datasets.
• Exposure to how data insights are translated into business recommendations.
• Guidance from experienced team members and opportunities to build applied data work.