Data Science Intern at InLighn Tech (InLighnX Global Pvt Ltd)

Job Description

Data Science Intern

Company: INLIGHN TECH
Location: Remote (100% Virtual)
Duration: 3 Months
Stipend for Top Interns: ₹15,000
Certificate Provided | Letter of Recommendation | Full-Time Offer Based on Performance

About the Company:

INLIGHN TECH empowers students and fresh graduates with real-world experience through hands-on, project-driven internships. The Data Science Internship is designed to equip you with the skills required to extract insights, build predictive models, and solve complex problems using data.
Role Overview:

As a Data Science Intern, you will work on real-world datasets to develop machine learning models, perform data wrangling, and generate actionable insights. This internship will help you strengthen your technical foundation in data science while working on projects that have a tangible business impact.
Key Responsibilities:

Collect, clean, and preprocess data from various sources
Apply statistical methods and machine learning techniques to extract insights
Build and evaluate predictive models for classification, regression, or clustering tasks
Visualize data using libraries like Matplotlib, Seaborn, or tools like Power BI
Document findings and present results to stakeholders in a clear and concise manner
Collaborate with team members on data-driven projects and innovations
Qualifications:

Pursuing or recently completed a degree in Data Science, Computer Science, Mathematics, or a related field
Proficiency in Python and data science libraries (NumPy, Pandas, Scikit-learn, etc.)
Understanding of statistical analysis and machine learning algorithms
Familiarity with SQL and data visualization tools or libraries
Strong analytical, problem-solving, and critical thinking skills
Eagerness to learn and apply data science techniques to solve real-world problems
Internship Benefits:

Hands-on experience with real datasets and end-to-end data science projects
Certificate of Internship upon successful completion
Letter of Recommendation for top performers
Build a strong portfolio of data science projects and models