Location
Noida, Uttar Pradesh
Work Mode
Hybrid
Duration
6 months
Stipend
₹22,000/mo
Start Date
Immediate / Rolling
Openings
14 positions
Type
Full-time
Category
Machine Learning
About the Internship
Join SBI Mutual Fund in Noida as an Anomaly Detection Intern. This entry-level internship is designed for students and recent graduates who learn best by building, shipping and getting real feedback on anomaly and fraud detection work.
About the role
As an Anomaly Detection Intern at SBI Mutual…
About SBI Mutual Fund
SBI Mutual Fund offers a mentor-led Anomaly Detection Intern in Noida with live project work in machine learning, a stipend of ₹22,000/month and a Pre-Placement Offer opportunity for strong performers.
See all SBI Mutual Fundinternships →Noida, Uttar PradeshVerified employer14 openings
Apply to SBI Mutual Fund →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 Noida. 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
Mentorship from a named mentorLive project experienceExposure to professional tools and workflowsPre-Placement Offer (PPO) opportunityFlexible working hoursStructured onboarding and a formal mentor at a large, established employer
Interview & Selection Process
4 rounds — typical selection flow for this role:
- 1Application screening & resume shortlisting by the talent team
- 2Practical assignment or case study with a short walkthrough
- 3Discussion with the hiring manager on the role and your fit
- 4HR discussion — stipend, start date, expectations and offer
₹22,000/month
Stipend · 6 months
Openings14 positions
Applicants1 applied
ModeHybrid
CategoryMachine Learning
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