Data Science & Applied AI Internship

Position Overview:

We are looking for a motivated Data Science Intern to join our Technology team and gain hands-on experience in building data-driven solutions for our Decision Intelligence platform. This internship offers an opportunity to learn the fundamentals of data science in a professional environment, working alongside experienced data scientists and machine learning engineers on real-world analytical projects.

Key Responsibilities:

1. Assist in collecting, cleaning, and organizing data from various internal and external sources to support analytical projects.
2. Perform basic exploratory data analysis (EDA) to uncover trends, patterns, and anomalies in datasets under the supervision of senior team members.
3. Support the development of statistical models and machine learning experiments by preparing data, running scripts, and documenting results.
4. Create simple data visualizations and summary reports to communicate preliminary findings to the team.
5. Assist in maintaining data documentation, metadata records, and analytical workflow logs.
6. Participate in team meetings, code reviews, and knowledge-sharing sessions to learn best practices in data science.
7. Conduct research on data science techniques, tools, and industry trends as assigned by the team.
8. Support senior data scientists with ad-hoc data requests, analysis tasks, and project documentation.

 

Essential Requirements:

• Currently pursuing or recently completed a Bachelor’s degree in Data Science, Computer Science, Statistics, Mathematics, or a related quantitative field.
• Basic knowledge of Python or R programming languages.
• Foundational understanding of statistics and probability concepts.
• Familiarity with data manipulation using libraries such as Pandas or NumPy.
• Knowledge of MS Office, particularly Excel.
• Strong analytical thinking and attention to detail.
• Eagerness to learn and ability to take constructive feedback.
• Good communication skills in English.

 

Preferred Qualifications:

• Completion of relevant coursework or online certifications in data science or machine learning.
• Exposure to SQL and relational databases.
• Familiarity with data visualization tools such as Tableau, Power BI, or Matplotlib.
• Experience with academic or personal data science projects (e.g., Kaggle competitions, capstone projects).
• Basic understanding of version control systems such as Git.