Location
Dehradun, Uttarakhand
Work Mode
Hybrid
Duration
3 months
Stipend
₹17,000/mo
Start Date
Immediate / Rolling
Openings
2 positions
Type
Full-time
Category
Machine Learning
About the Internship
Join Altigreen Propulsion Labs in Dehradun as a Time Series Forecasting Intern. This entry-level internship is designed for students and recent graduates who learn best by building, shipping and getting real feedback on time-series forecasting work.
About the role
As a Time Series Forecasting…
About Altigreen Propulsion Labs
Altigreen Propulsion Labs offers a mentor-led Time Series Forecasting Intern in Dehradun with live project work in machine learning, a stipend of ₹17,000/month and a Pre-Placement Offer opportunity for strong performers.
See all Altigreen Propulsion Labsinternships →Dehradun, UttarakhandVerified employer2 openings
Apply to Altigreen Propulsion Labs →Skills Required
Time-Series AnalysisARIMA & ProphetPythonSeasonality & Trend DecompositionForecast Accuracy (MAPE / RMSE)Feature EngineeringPandasStatistical TestingBacktestingData Visualisation
Who Can Apply
- Final-year students and recent graduates from B.E./B.Tech, B.Sc/M.Sc (Statistics / Mathematics / Economics) or MCA who are based in or willing to work from Dehradun. You should be keen to learn Time-Series Analysis, ARIMA & Prophet, Python 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
Mentorship from a named mentorLive project experienceExposure to professional tools and workflowsPre-Placement Offer (PPO) opportunityFlexible working hours
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
₹17,000/month
Stipend · 3 months
Openings2 positions
Applicants0 applied
ModeHybrid
CategoryMachine Learning
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