Data Science Intern

About the job

Money Fellows is transforming how people save, borrow, and invest. As the world’s first and largest Money Circles app, we’ve digitized the traditional ROSCA model to make it smarter, safer, and more accessible. 💸
Since 2016, we’ve empowered over 10+ million users to take control of their financial futures with confidence. 🚀

• Support the analysis of financial and business datasets to identify patterns, trends, correlations, and potential insights that support business understanding.
• Assist in cleaning, transforming, and organizing structured and unstructured datasets to ensure data quality and readiness for analysis and modeling.
• Apply basic statistical techniques and exploratory data analysis to investigate business questions and identify opportunities for further analysis.
• Assist in developing, training, and testing basic machine learning models under the guidance of senior team members.
• Support model validation and performance evaluation using appropriate metrics and techniques, documenting results and highlighting areas for improvement.
• Prepare basic charts, dashboards, and reports to communicate analytical findings and insights to technical and non-technical stakeholders.
• Work with Data Science, Data Engineering, and Software Engineering teams to support assigned analytical projects and understand

• Basic understanding of statistical analysis, probability, and machine learning concepts.
• Good knowledge of Python or R, with familiarity with data manipulation libraries such as Pandas.
• Basic understanding of machine learning algorithms, including regression, classification, and clustering.
• Familiarity with machine learning libraries and frameworks such as Scikit-learn, TensorFlow, or PyTorch is an advantage.
• Basic knowledge of SQL and relational databases for data extraction and manipulation.
• Familiarity with data visualization tools and libraries such as Matplotlib, Seaborn, or Power BI.
• Basic understanding of data cleaning, preprocessing, and feature engineering techniques.
• Strong analytical thinking, problem-solving, and quantitative reasoning skills.