Location
Bhubaneswar, Odisha
Work Mode
Hybrid
Duration
2 months
Stipend
₹23,000/mo
Start Date
Immediate / Rolling
Openings
14 positions
Type
Full-time
Category
Machine Learning
About the Internship
Deloitte India is hiring a Time Series Forecasting Intern in Bhubaneswar — a hands-on, mentor-led internship built for students and 2026 freshers who want real exposure to time series analysis and forecasting rather than a certificate-only programme.
About the role
As a Time Series Forecasting…
About Deloitte India
Deloitte India offers a mentor-led Time Series Forecasting Intern in Bhubaneswar with live project work in machine learning, a stipend of ₹23,000/month and a Pre-Placement Offer opportunity for strong performers.
See all Deloitte Indiainternships →Bhubaneswar, OdishaVerified employer14 openings
Apply to Deloitte India →Skills Required
Time Series AnalysisARIMA & ProphetSeasonalityBacktestingPythonPandasStatisticsFeature EngineeringForecast Accuracy (MAPE)Visualisation
Who Can Apply
- Final-year students and recent graduates from B.E./B.Tech (any branch), MCA, M.Sc (Statistics / Mathematics / Economics) or equivalent who are based in or willing to work from Bhubaneswar. You should be keen to learn Time Series Analysis, ARIMA & Prophet, Seasonality 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 CertificateStructured onboarding and a formal mentor at a large, established employer
Interview & Selection Process
4 rounds — typical selection flow for this role:
- 1Application screening & resume shortlisting by the talent team
- 2Online aptitude / domain assessment
- 3Technical interview on fundamentals and your projects
- 4HR discussion — stipend, start date, expectations and offer
₹23,000/month
Stipend · 2 months
Openings14 positions
Applicants1 applied
ModeHybrid
CategoryMachine Learning
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