Data Science & Applied AI Internship

Position Overview: We are looking for a motivated Data Science Intern to join our Technology team starting from 1-Oct-2026 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: - Assist in collecting, cleaning, and organizing data from various internal and external sources to support analytical projects. - Perform basic exploratory data analysis (EDA) to uncover trends, patterns, and anomalies in datasets under the supervision of senior team members. - Support the development of statistical models and machine learning experiments by preparing data, running scripts, and documenting results. - Create simple data visualizations and summary reports to communicate preliminary findings to the team. - Assist in maintaining data documentation, metadata records, and analytical workflow logs. - Participate in team meetings, code reviews, and knowledge-sharing sessions to learn best practices in data science. - Conduct research on data science techniques, tools, and industry trends as assigned by the team. - 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.