Data Scientist Internship

Role Overview: The Data Scientist Intern will assist in data collection, preprocessing, analysis, and visualization to support data-driven decision-making. This role provides an excellent opportunity to work with real-world datasets, develop machine learning models, and contribute to meaningful projects under the guidance of experienced data scientists.

Key Responsibilities:

• Data Collection & Preprocessing: Gather, clean, and preprocess structured and unstructured data from various sources (databases, APIs, spreadsheets, etc.).
• Exploratory Data Analysis (EDA): Perform statistical analysis and visualization to uncover trends, patterns, and insights.
• Machine Learning & Modeling: Assist in building, testing, and optimizing machine learning models for predictive analytics.
• Data Visualization & Reporting: Create dashboards, charts, and reports using visualization tools like Tableau, Power BI, Matplotlib, or Seaborn.
• Collaboration: Work with cross-functional teams, including software engineers and product managers, to support data-driven projects.
• Documentation & Research: Document data processing workflows, methodologies, and key findings for future reference.
• Continuous Learning: Stay updated with industry trends, tools, and techniques to enhance analytical skills.

Qualifications & Requirements:

• Currently pursuing or recently completed a degree in Data Science, Computer Science, Statistics, Mathematics, or a related field.
• Proficiency in Python or R for data analysis and machine learning.
• Familiarity with data manipulation libraries (Pandas, NumPy, SciPy, etc.).
• Basic knowledge of machine learning frameworks (scikit-learn, TensorFlow, PyTorch, etc.).
• Experience with SQL for querying databases and extracting data.
• Strong analytical skills with an understanding of statistics and probability.
• Ability to create data visualizations and interpret insights effectively.
• Strong communication and problem-solving skills.
• Enthusiasm to learn and apply new technologies in a fast-paced environment.