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M2P Fintech logo — M2P Fintech Fraud Detection ML Intern in Pune, Maharashtra
M2P Fintech VerifiedActively Hiring

Fraud Detection ML Intern

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Location

Pune, Maharashtra

Work Mode

Hybrid

Duration

3 months

Stipend

₹32,000/mo

Start Date

Immediate / Rolling

Openings

12 positions

Type

Full-time

Category

Machine Learning

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About the Internship

Join M2P Fintech in Pune as a Fraud Detection ML Intern. This entry-level internship is designed for students and recent graduates who learn best by building, shipping and getting real feedback on fraud and anomaly detection work. About the role As a Fraud Detection ML Intern at M2P Fintech, your…

About M2P Fintech

M2P Fintech offers a mentor-led Fraud Detection ML Intern in Pune with live project work in machine learning, a stipend of ₹32,000/month and a Pre-Placement Offer opportunity for strong performers.
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Pune, MaharashtraVerified employer12 openings
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Skills Required

Anomaly DetectionPythonImbalanced Data TechniquesFeature EngineeringScikit-LearnSQLPrecision-Recall TradeoffsRule + Model HybridsModel ExplainabilityDomain Investigation

Who Can Apply

  • Final-year students and recent graduates from B.E./B.Tech, MCA, M.Sc (Statistics / Data Science) or MBA (Analytics) who are based in or willing to work from Pune. You should be keen to learn Anomaly Detection, Python, Imbalanced Data Techniques 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

Flexible working hoursNetworking with senior professionalsInternship CertificateLetter of RecommendationMentorship from a named mentor

Interview & Selection Process

4 rounds — typical selection flow for this role:

  1. 1Application screening & resume shortlisting by the talent team
  2. 2Online aptitude / domain assessment
  3. 3Technical interview on fundamentals and your projects
  4. 4HR discussion — stipend, start date, expectations and offer
32,000/month

Stipend · 3 months

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Openings12 positions
Applicants0 applied
ModeHybrid
CategoryMachine Learning
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