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Unacademy logo — Unacademy Fraud Detection ML Intern in Faridabad, Haryana
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Fraud Detection ML Intern

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Location

Faridabad, Haryana

Work Mode

In Office

Duration

2 months

Stipend

₹31,000/mo

Start Date

Immediate / Rolling

Openings

9 positions

Type

Full-time

Category

Machine Learning

Apply Now — ₹31,000/moApply on Company Website ↗

About the Internship

Unacademy has an open Fraud Detection ML Intern position in Faridabad for motivated freshers and final-year students. You will work on live fraud and anomaly detection projects and finish with something you can genuinely talk about in interviews. About the role As a Fraud Detection ML Intern at…

About Unacademy

Unacademy offers a mentor-led Fraud Detection ML Intern in Faridabad with live project work in machine learning, a stipend of ₹31,000/month and a Pre-Placement Offer opportunity for strong performers.
Faridabad, HaryanaVerified employer9 openings
Apply to Unacademy

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 Faridabad. 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

Live project experienceExposure to professional tools and workflowsPre-Placement Offer (PPO) opportunityFlexible working hoursNetworking with senior professionals

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
31,000/month

Stipend · 2 months

Apply Now →
Openings9 positions
Applicants0 applied
ModeIn Office
CategoryMachine Learning
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