Data Scientist Intern

We are looking for a motivated Data Science Intern to join our team. In this role, you will gain hands-on experience working with real-world data, applying statistical and machine learning techniques, and contributing to data-driven decision-making. This internship is an excellent opportunity for students or recent graduates who are eager to develop their technical skills and gain industry experience in data science.

Key Responsibilities:

• Data Analysis and Exploration: Assist in analyzing large datasets to uncover trends and insights. Support data interpretation and communicate findings to both technical and non-technical stakeholders.
• Machine Learning and Predictive Modeling: Work with the team to develop and experiment with machine learning models. Help evaluate model performance and optimize algorithms for better accuracy.
• Data Cleaning and Preprocessing: Assist in cleaning and preparing raw data for analysis and modeling. Address missing or inconsistent data to maintain high data quality.
• Statistical Analysis and Experimentation: Conduct basic statistical analysis to validate hypotheses. Work on designing and analyzing experiments to support business decisions.
• Feature Engineering and Data Transformation: Explore and create relevant features to improve model performance. Learn about feature selection techniques to enhance predictive accuracy.
• Data Visualization and Communication: Create visualizations using tools like PowerBI, matplotlib, or seaborn to present data insights. Assist in preparing reports and presentations for stakeholders.
• Collaboration and Learning: Work closely with data scientists, engineers, and business teams to understand data needs. Stay updated with the latest data science trends and technologies.

Qualifications:

• Education: Currently pursuing or recently completed a Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Mathematics, or a related field.
• Skills and Experience: Familiarity with Python or R for data analysis. Experience with Pandas or similar libraries. Basic understanding of supervised and unsupervised learning. Experience with PowerBI, matplotlib, or seaborn. Exposure to SQL and database querying. Understanding of basic statistical concepts and hypothesis testing.