Location
Pimpri-Chinchwad, Maharashtra
Work Mode
In Office
Duration
6 months
Stipend
₹16,000/mo
Start Date
Immediate / Rolling
Openings
12 positions
Type
Full-time
Category
Machine Learning
About the Internship
Join Capillary Technologies in Pimpri-Chinchwad 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…
About Capillary Technologies
Capillary Technologies offers a mentor-led Fraud Detection ML Intern in Pimpri-Chinchwad with live project work in machine learning, a stipend of ₹16,000/month and a Pre-Placement Offer opportunity for strong performers.
See all Capillary Technologiesinternships →Pimpri-Chinchwad, MaharashtraVerified employer12 openings
Apply to Capillary Technologies →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 Pimpri-Chinchwad. 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:
- 1Application screening & resume shortlisting by the talent team
- 2Technical interview on fundamentals and your projects
- 3Practical assignment or case study with a short walkthrough
- 4HR discussion — stipend, start date, expectations and offer
₹16,000/month
Stipend · 6 months
Openings12 positions
Applicants0 applied
ModeIn Office
CategoryMachine Learning
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