Data Science Intern
@ WEBBOOST SOLUTION IT SERVICES

Hybrid
₹10,000
Hybrid
Intern
Posted 21 days ago

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XXXXXXXX XXXXXXXXXXXXX XXXXXX***** @webboostsolutions.com
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Job Details

About Data Science Intern

WebBoost Solutions by UM offers hands-on data science internships. Gain real-world experience in data analysis, model development, and machine learning in a remote setting.

Responsibilities

  • Collect, preprocess, and analyze large datasets.
  • Develop predictive models and machine learning algorithms.
  • Perform exploratory data analysis (EDA) to extract insights.
  • Create data visualizations and dashboards.
  • Collaborate with cross-functional teams for data-driven solutions.

Requirements

  • Enrolled in or graduate of a relevant program.
  • Proficiency in Python for data analysis and modeling.
  • Experience with machine learning libraries such as scikit-learn, TensorFlow, or PyTorch.
  • Familiarity with data visualization tools like Tableau, Power BI, or Matplotlib.
  • Strong analytical, problem-solving, and communication skills.

Stipend & Benefits

  • Stipend: ₹7,500 - ₹15,000 (Performance-Based)
  • Hands-on experience with data science projects
  • Certificate of Internship & Letter of Recommendation
  • Portfolio building opportunities
  • Potential for full-time employment based on performance

How to Apply

Submit your resume and cover letter with the subject line "Data Science Intern Application" before 14th October 2025.

Equal Opportunity

WebBoost Solutions by UM is committed to fostering an inclusive and diverse environment. Applications from all backgrounds are encouraged.

Key skills/competency

  • Data Analysis
  • Machine Learning
  • Python
  • EDA
  • Data Visualization
  • Predictive Modeling
  • Problem Solving
  • Team Collaboration
  • Communication
  • Dashboard Creation
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How to Get Hired at WEBBOOST SOLUTION IT SERVICES

🎯 Tips for Getting Hired

  • Research WebBoost Solutions by UM: Understand the company mission and projects.
  • Tailor your resume: Highlight data science and Python skills.
  • Prepare portfolio work: Showcase real projects and models.
  • Practice technical questions: Review machine learning and EDA concepts.

📝 Interview Preparation Advice

Technical Preparation

Review Python libraries usage.
Practice machine learning model building.
Study data pre-processing techniques.
Familiarize with data visualization tools.

Behavioral Questions

Describe overcoming project challenges.
Explain effective teamwork in past projects.
Discuss handling tight deadlines.
Share how you learn new tools.

Frequently Asked Questions