Data Science - Internship (WFH)

About the internship:Responsibilities

Build lead scoring models to prioritise which enquiries admissions should call first

Analyse learner engagement data from the LMS to predict dropout risk

Work on recommendation logic for programme and specialisation suggestions

Translate model output into decisions the marketing and academic teams can act on

Document methodology so every result is reproducibleWho can apply:Only those candidates can apply who:are available for the work from home job/internshipcan start the work from home job/internship between 23rd Sep'26 and 28th Oct'26are available for duration of 6 monthshave relevant skills and interests* Freshers looking for jobs may also applyStipend:INR₹ 15,000 /monthDeadline:[hidden]:59:59Other perks:Certificate, Letter of recommendation, Flexible work hours, 5 days a week, Job offerSkills required:Python, SQL, Data Analytics, Machine Learning and Data ScienceAbout Company:Reducate.ai is a diversified technology-led group founded in 2019 by alumni of IIT BHU and IIM Ahmedabad. Operating across education, artificial intelligence, manufacturing, and fashion, the group's mission is to build scalable, future-ready businesses from India for the world. Recognized for innovation and impact, Reducate.ai has been featured among ET Times 40 Under 40 leaders and Forbes 200 emerging companies. Its ecosystem includes Coding Jr, enabling K-12 schools with coding and AI curricula; Coding Pro, partnering with universities to create Centers of Excellence in AI and Data Science; ItsCredible, an agentic HR and digital credentialing platform; Planto.ai, an enterprise AI OS for copilots and automation; and Elyndra, a premium diamond jewellery brand. Reducate.ai is powered by 50+ professionals from IITs, IIMs, and NIFT, operating with a lean, execution-focused culture and a growing global footprint.Additional information:Job offer: On successful conversion to a permanent employee, the candidate can expect a salary of ₹ 600000 to 1200000/year

Responsibilities

Responsibilities

Build lead scoring models to prioritise which enquiries admissions should call first

Analyse learner engagement data from the LMS to predict dropout risk

Work on recommendation logic for programme and specialisation suggestions

Translate model output into decisions the marketing and academic teams can act on

Document methodology so every result is reproducible