Machine Learning Intern

About the Job

We are onboarding Data & ML Interns to work on real-world datasets, machine learning models, and predictive analytics workflows while learning and gaining practical experience in ML and data science.

Roles & Responsibilities

• Work with datasets using Python, Scikit-learn, and ML frameworks

• Perform data analysis, cleaning, and machine learning tasks

• Build predictive models and ML pipelines

• Generate insights from structured and unstructured data

• Support ML research and predictive analytics workflows

• Collaborate on practical ML projects and datasets

Eligibility Criteria

• Freshers and college students only

• BCA / B.Tech / B.E. / B.Sc. (IT) / M.Sc. (IT) / MCA students or graduates

• Basic knowledge of Python, ML, or analytical thinking

• Interested in machine learning, data science, and predictive analytics

• Willingness to learn through practical execution

Internship Details

• Mode: Remote / Virtual

• Recommended Duration: 3–6 Months

• Practical exposure to ML and predictive analytics workflows

• Stipend: upto Rs. 7500

Growth Opportunities

• Exposure to real time ML workflows and data science projects

• Advanced ML and predictive analytics project opportunities

• Mentorship on real-world ML and predictive analytics workflow

• Internship Completion Certificate

• Practical ML and predictive analytics experience

Apply now using the Apply Button

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PrimrIQ collects and processes applicant information solely for recruitment, internship selection, and related communication. Your information may be reviewed by authorized members of the PrimrIQ team for evaluating your application and contacting you regarding the opportunity. We do not sell applicant information to third parties. Applicant information is handled securely and retained only for as long as reasonably necessary for recruitment and related purposes, subject to applicable laws and regulations.

Equal Opportunity Employer

PrimrIQ is an equal opportunity organization. We are committed to providing a fair and inclusive environment and do not discriminate against applicants based on gender, age, disability, religion, race, caste, nationality, or any other protected characteristic. Selection is based on qualifications, skills, experience, and suitability for the role.