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Nasdaq logo — Nasdaq Time Series Forecasting Intern in Hyderabad, Telangana
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Time Series Forecasting Intern

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Location

Hyderabad, Telangana

Work Mode

In Office

Duration

6 months

Stipend

₹30,000/mo

Start Date

Immediate / Rolling

Openings

13 positions

Type

Full-time

Category

Machine Learning

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About the Internship

Start your career in time-series forecasting with Nasdaq as a Time Series Forecasting Intern in Hyderabad. You will sit with a working team, use the same tools and systems they do, and own a defined piece of work from day one. About the role As a Time Series Forecasting Intern at Nasdaq, your core…

About Nasdaq

Nasdaq offers a mentor-led Time Series Forecasting Intern in Hyderabad with live project work in machine learning, a stipend of ₹30,000/month and a Pre-Placement Offer opportunity for strong performers.
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Hyderabad, TelanganaVerified employer13 openings
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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 Hyderabad. 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

Letter of RecommendationMentorship from a named mentorLive project experienceExposure to professional tools and workflowsPre-Placement Offer (PPO) opportunity

Interview & Selection Process

4 rounds — typical selection flow for this role:

  1. 1Application screening & resume shortlisting by the talent team
  2. 2Team-fit / behavioural interview with the team
  3. 3Online aptitude / domain assessment
  4. 4HR discussion — stipend, start date, expectations and offer
30,000/month

Stipend · 6 months

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Openings13 positions
Applicants0 applied
ModeIn Office
CategoryMachine Learning
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