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Rocketlane logo — Rocketlane Feature Engineering Intern in Pune, Maharashtra
Rocketlane VerifiedActively Hiring

Feature Engineering Intern

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Location

Pune, Maharashtra

Work Mode

Hybrid

Duration

2 months

Stipend

₹18,000/mo

Start Date

Immediate / Rolling

Openings

15 positions

Type

Full-time

Category

Machine Learning

Apply Now — ₹18,000/moApply on Company Website ↗

About the Internship

Rocketlane is hiring a Feature Engineering Intern in Pune — a hands-on, mentor-led internship built for students and 2026 freshers who want real exposure to feature engineering for machine learning rather than a certificate-only programme. About the role As a Feature Engineering Intern at…

About Rocketlane

Rocketlane offers a mentor-led Feature Engineering Intern in Pune with live project work in machine learning, a stipend of ₹18,000/month and a Pre-Placement Offer opportunity for strong performers.
Pune, MaharashtraVerified employer15 openings
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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 Pune. 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

Exposure to professional tools and workflowsPre-Placement Offer (PPO) opportunityFlexible working hoursNetworking with senior professionalsInternship CertificateBroad ownership and direct exposure to founders and senior decision-makers

Interview & Selection Process

2 rounds — typical selection flow for this role:

  1. 1Application screening & resume shortlisting by the talent team
  2. 2Online aptitude / domain assessment
18,000/month

Stipend · 2 months

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Openings15 positions
Applicants1 applied
ModeHybrid
CategoryMachine Learning
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