External

Data Science Intern

🏢 Zenithbyte  •  📍 India

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Job Description

Data Science Intern (Paid) Company: WebBoost IT Services Location: Remote Duration: 3 Months Opportunity: Full-time based on performance + Certificate of Internship About WebBoost IT Services WebBoost IT Services provides aspiring data professionals with hands-on experience in data science. Through real-world projects, mentorship, and practical exposure, we help students and graduates develop strong analytical, statistical, and machine learning skills essential for a successful career in the data industry. Responsibilities ✅ Collect, clean, preprocess, and analyze large datasets. ✅ Develop predictive models and machine learning algorithms. ✅ Perform Exploratory Data Analysis (EDA) to extract insights. ✅ Create impactful data visualizations and dashboards. ✅ Collaborate with cross-functional teams to deliver data-driven solutions and recommendations. Requirements 🎓 Enrolled in or graduate of a Data Science, Computer Science, Statistics, or related program. 🐍 Proficiency in Python for data analysis and machine learning. 🧠 Knowledge of ML libraries such as scikit-learn, TensorFlow, or PyTorch (preferred). 📊 Familiarity with data visualization tools like Tableau, Power BI, or Matplotlib . 🧐 Strong analytical, critical thinking, and problem-solving abilities. 🗣 Excellent communication and teamwork skills. Stipend & Benefits 💰 Stipend: ₹7,500 - ₹15,000 (Performance-Based) ✔ Real-world data science project experience. ✔ Certificate of Internship & Letter of Recommendation. ✔ Opportunity to build a strong portfolio of ML models and analytical solutions. ✔ Potential full-time employment based on performance. How to Apply 📩 Send your resume and cover letter with the subject line: “Data Science Intern Application – WebBoost IT Services” 📅 Application Deadline: 10th December 2025 Equal Opportunity Statement WebBoost IT Services is committed to fostering an inclusive, diverse, and growth-oriented environment. We welcome applicants from all backgrounds to apply.
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