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Karnataka Bank logo — Karnataka Bank Anomaly Detection Intern in Kochi, Kerala
Karnataka Bank VerifiedActively Hiring

Anomaly Detection Intern

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Location

Kochi, Kerala

Work Mode

Hybrid

Duration

2 months

Stipend

₹16,000/mo

Start Date

Immediate / Rolling

Openings

10 positions

Type

Full-time

Category

Machine Learning

Apply Now — ₹16,000/moApply on Company Website ↗

About the Internship

Karnataka Bank is hiring an Anomaly Detection Intern in Kochi — a hands-on, mentor-led internship built for students and 2026 freshers who want real exposure to anomaly and fraud detection rather than a certificate-only programme. About the role As an Anomaly Detection Intern at Karnataka Bank…

About Karnataka Bank

Karnataka Bank offers a mentor-led Anomaly Detection Intern in Kochi with live project work in machine learning, a stipend of ₹16,000/month and a Pre-Placement Offer opportunity for strong performers.
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Kochi, KeralaVerified employer10 openings
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Skills Required

Anomaly DetectionImbalanced DataPrecision & RecallPythonScikit-learnFeature EngineeringSQLStatisticsThreshold TuningEvaluation

Who Can Apply

  • Final-year students and recent graduates from B.E./B.Tech (any branch), MCA or M.Sc (Statistics / Mathematics / Computer Science) who are based in or willing to work from Kochi. You should be keen to learn Anomaly Detection, Imbalanced Data, Precision & Recall 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 workflowsStructured onboarding and a formal mentor at a large, established employer

Interview & Selection Process

2 rounds — typical selection flow for this role:

  1. 1Application screening & resume shortlisting by the talent team
  2. 2Practical assignment or case study with a short walkthrough
16,000/month

Stipend · 2 months

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Openings10 positions
Applicants0 applied
ModeHybrid
CategoryMachine Learning
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