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Zeta logo — Zeta Recommendation Systems Intern in Mumbai, Maharashtra
Zeta VerifiedActively Hiring

Recommendation Systems Intern

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Location

Mumbai, Maharashtra

Work Mode

Hybrid

Duration

4 months

Stipend

₹31,000/mo

Start Date

Immediate / Rolling

Openings

12 positions

Type

Full-time

Category

Machine Learning

Apply Now — ₹31,000/moApply on Company Website ↗

About the Internship

Zeta is hiring a Recommendation Systems Intern in Mumbai — a hands-on, mentor-led internship built for students and 2026 freshers who want real exposure to recommendation and personalisation systems rather than a certificate-only programme. About the role As a Recommendation Systems Intern at…

About Zeta

Zeta offers a mentor-led Recommendation Systems Intern in Mumbai with live project work in machine learning, a stipend of ₹31,000/month and a Pre-Placement Offer opportunity for strong performers.
Mumbai, MaharashtraVerified employer12 openings
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Skills Required

Recommendation SystemsCollaborative FilteringPythonRanking ModelsEmbeddingsA/B TestingSQLCold-Start HandlingEvaluation (NDCG / CTR)Feature Engineering

Who Can Apply

  • Final-year students and recent graduates from B.E./B.Tech (CSE/IT), MCA, M.Sc (CS / Statistics) or M.Tech who are based in or willing to work from Mumbai. You should be keen to learn Recommendation Systems, Collaborative Filtering, Python on real projects and able to write clearly. Freshers are welcome — no prior work experience is required, and a strong project portfolio matters more than a long CV.

Perks

Internship CertificateLetter of RecommendationMentorship from a named mentorLive project experienceExposure to professional tools and workflows

Interview & Selection Process

3 rounds — typical selection flow for this role:

  1. 1Application screening & resume shortlisting by the talent team
  2. 2Technical interview on fundamentals and your projects
  3. 3HR discussion — stipend, start date, expectations and offer
31,000/month

Stipend · 4 months

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Openings12 positions
Applicants0 applied
ModeHybrid
CategoryMachine Learning
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