DeHaat

Data Science Intern

InternshipIn OfficeNoida, Uttar Pradesh6 months26K/mo3 openingsImmediate / Rolling

About this internship

Join DeHaat in Noida as a Data Science Intern. This entry-level internship is designed for students and recent graduates who learn best by building, shipping and getting real feedback on data and analytics work. About the role As a Data Science Intern at DeHaat, your core focus will be turning raw…

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Skills you'll use

PythonMachine LearningStatisticsPandasSQLData VisualisationFeature EngineeringModel EvaluationJupyterStorytelling with Data

What you'll do

Work on turning raw data into decisions people act on alongside the core team
Apply Python, Machine Learning, Statistics, Pandas to live, production-facing problems
Document your work clearly for reviewers and stakeholders
Participate in reviews and act on the feedback you receive
Collaborate across engineering, product, design or business as required
Follow the team's quality, testing and documentation standards

Who can apply

Pursuing or recently completed B.E./B.Tech/M.Sc (Statistics, Maths)/MCA/MBA (Analytics) — 2026 freshers & final-year students welcome
Working knowledge of Python, Machine Learning, Statistics (or a genuine willingness to learn them)
Strong fundamentals in your discipline and clear problem-solving ability
Care with detail and the discipline to document your work
Clear written and verbal communication in English
Based in or willing to work from Noida, Uttar Pradesh

Perks & benefits

Flexible working hoursNetworking with senior professionalsInternship CertificateLetter of RecommendationMentorship from a named mentor

Interview & selection process

3 rounds — typical selection flow for this role:

1Application screening & resume shortlisting by the talent team
2Technical interview on fundamentals and your projects
3HR discussion — stipend, start date, expectations and offer

About DeHaat

DeHaat operates in the agritech sector and has an active presence in Noida. Interns here are put on real work from the start, because the team's view is that the fastest way to learn a craft is to practise it under people who already do it well — not to watch it from a distance. The work runs at…

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