Data Science Intern
Dexter's Tech
Job description
Data Science Intern (Paid)
Company: Dexter’s Tech
Location: Remote
Duration: 3 Months
Opportunity & Benefits
- 💰 Performance-based stipend up to ₹7,499
- 🎁 Swags and goodies
- 📜 Internship Certificate
- ✉️ Letter of Recommendation (based on performance)
- 🌟 Potential full-time role based on performance
- 📈 Hands-on experience with real-world data science projects
About Dexter’s Tech
Dexter’s Tech empowers aspiring data professionals by providing practical, industry-relevant experience in data science. The internship focuses on building strong foundations in analytics, statistics, and machine learning through live projects aligned with real-world business and technology challenges.
Roles & Responsibilities
- Collect, preprocess, and analyze large datasets
- Perform Exploratory Data Analysis (EDA) to uncover meaningful insights
- Build predictive models and machine learning algorithms
- Create data visualizations and dashboards for effective storytelling
- Collaborate with cross-functional teams to deliver data-driven solutions
Requirements
- Currently enrolled in or graduated from Data Science, Computer Science, Statistics, or related fields
- Proficiency in Python or R for data analysis and modeling
- Familiarity with scikit-learn, TensorFlow, or PyTorch (preferred)
- Experience with visualization tools such as Tableau, Power BI, or Matplotlib
- Strong analytical, problem-solving, and communication skills
What You Will Gain
- Real-world exposure to data science workflows
- Performance-based stipend up to ₹7,499
- Swags and goodies
- Internship Certificate upon completion
- Letter of Recommendation based on performance
- Opportunity to build a strong data science portfolio
- Potential full-time role based on performance
How to Apply
Submit your resume and cover letter with the subject line:
“Data Science Intern Application”
📅 Application Deadline: 15th January 2026
Equal Opportunity Statement
Dexter’s Tech is an equal opportunity organization and encourages applications from candidates of all backgrounds.