Data Science Intern

About the Role

Company: Thinknovaa

• Duration: 3 Months
• Work Mode: Remote
• Stipend: Up to ₹10,000 (Performance-Based)

About the Internship

We are looking for enthusiastic individuals interested in Data Science. This internship offers an opportunity to gain practical experience in data analysis, statistical modeling, predictive analytics, machine learning, and data visualization through hands-on projects using modern data science technologies.

Responsibilities

• Assist in collecting, cleaning, preprocessing, and analyzing structured and unstructured datasets.
• Perform Exploratory Data Analysis (EDA) to identify trends, patterns, and meaningful insights.
• Develop, train, and evaluate Machine Learning models for predictive analytics.
• Apply statistical techniques and data science methodologies to solve practical problems.
• Work with Python and popular data science libraries for data manipulation and modeling.
• Create data visualizations, reports, and dashboards to communicate analytical findings.
• Perform feature engineering, model evaluation, and performance optimization.
• Collaborate with team members on assigned data science projects.

Requirements

• Basic understanding of Data Science, statistics, and Machine Learning concepts.
• Familiarity with Python programming.
• Knowledge of Pandas, NumPy, Matplotlib, Seaborn, or Scikit-learn is an advantage.
• Basic understanding of data cleaning, data preprocessing, and Exploratory Data Analysis (EDA).
• Familiarity with SQL, Excel, Power BI, or Tableau is a plus.
• Interest in predictive modeling, Artificial Intelligence, and data-driven decision-making.
• Strong analytical thinking and problem-solving skills.
• Good communication skills and willingness to learn.
• Ability to work independently in a remote environment.

Who Can Apply?

Students, freshers, graduates, working professionals, self-taught learners, and career switchers interested in Data Science are welcome to apply. Join Thinknovaa to strengthen your data science skills, work on practical analytics and machine learning projects, enhance your professional portfolio, and gain valuable experience in data-driven technologies.