Location
Visakhapatnam, Andhra Pradesh
Work Mode
Hybrid
Duration
3 months
Stipend
₹16,000/mo
Start Date
Immediate / Rolling
Openings
15 positions
Type
Full-time
Category
Machine Learning
About the Internship
Join Kotak Mahindra Bank in Visakhapatnam as a Feature Engineering Intern. This entry-level internship is designed for students and recent graduates who learn best by building, shipping and getting real feedback on feature engineering for machine learning work.
About the role
As a Feature…
About Kotak Mahindra Bank
Kotak Mahindra Bank offers a mentor-led Feature Engineering Intern in Visakhapatnam with live project work in machine learning, a stipend of ₹16,000/month and a Pre-Placement Offer opportunity for strong performers.
See all Kotak Mahindra Bankinternships →Visakhapatnam, Andhra PradeshVerified employer15 openings
Apply to Kotak Mahindra Bank →Skills Required
Feature EngineeringData Leakage DetectionPandasSQLPythonFeature SelectionScaling & EncodingCross-ValidationStatisticsDocumentation
Who Can Apply
- Final-year students and recent graduates from B.E./B.Tech (any branch), BCA/MCA or B.Sc/M.Sc (Statistics / Computer Science / Mathematics) who are based in or willing to work from Visakhapatnam. You should be keen to learn Feature Engineering, Data Leakage Detection, Pandas 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
3 rounds — typical selection flow for this role:
- 1Application screening & resume shortlisting by the talent team
- 2Technical interview on fundamentals and your projects
- 3HR discussion — stipend, start date, expectations and offer
₹16,000/month
Stipend · 3 months
Openings15 positions
Applicants0 applied
ModeHybrid
CategoryMachine Learning
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