Kukbit SL has listed a paid Data Science internship on Unstop with a work-from-home arrangement and a monthly stipend of ₹18,000–20,000. The advertised duration is two months, and the role is listed as full time with a five-day working week. It may interest students looking for practical exposure to Python, data processing and machine-learning work without an in-office location requirement.
The opportunity was checked on 26 September 2026 and was accepting registrations. The registration deadline is 8 October 2026 at approximately 4:16 PM IST. The page also shows a later time for the shortlisting round; applicants should use the registration deadline for submission and should not assume they can apply until the round ends. This distinction matters when planning an application near the last day.
Key internship information
- Employer listed: Kukbit SL.
- Position: Data Science intern.
- Mode: work from home.
- Duration: two months.
- Timing: full time.
- Workweek: five days.
- Advertised stipend: ₹18,000–20,000 per month.
- Registration closes: 8 October 2026, approximately 4:16 PM IST.
- Application platform: Unstop.
- Listed benefits include a certificate, recommendation letter and flexible hours.
- Source status at verification: live, with registration open.
What the employer has described
The notice outlines work on data-science algorithms and artificial-intelligence concepts, web scraping using Python or R, AI model development and processing structured or unstructured data. It is a short description rather than a detailed project brief. Applicants should therefore avoid assuming a particular client, dataset, deployment environment or daily assignment that the employer has not identified.
The listing's skill labels include Python and machine-learning concepts. A relevant applicant should be able to explain their actual programming and data experience, whether gained through coursework, personal projects or earlier work. The public listing does not set a numerical professional-experience minimum. It also does not publish a specific percentage requirement or a detailed academic selection formula.
Eligibility and availability
The platform displays broad student eligibility categories, including engineering students and undergraduate or postgraduate applicants. Candidates should still inspect the live eligibility section and complete the application accurately, because the employer and platform determine whether an individual registration meets their requirements. A broad category label does not guarantee that every applicant will be shortlisted.
Full-time availability and a two-month commitment are the practical conditions to consider first. Flexible hours are listed as a benefit, but that does not turn the internship into a part-time role. Students with classes, examinations or another job should ask about daily expectations, meetings and supervision before accepting. The notice does not provide a precise daily schedule or an announced joining date.
Stipend and benefits: what is confirmed
The advertised monthly range is ₹18,000–20,000. No separate fixed-versus-incentive breakdown appears in the checked listing. Candidates should obtain the exact offered amount and payment terms in writing rather than assume that everyone receives the maximum. The page does not explain how the employer chooses an amount within the range or whether any deductions apply.
A certificate, letter of recommendation and flexible hours are mentioned. A pre-placement-opportunity label also appears among the platform benefits, but no guaranteed permanent role, conversion condition or annual salary is specified. Treat any future employment possibility as something to clarify, not a confirmed outcome of completing the internship. Equipment provision and internet reimbursement are also not stated.
Preparing for the data work
The remainder of this preparation section is editorial guidance, not an employer-issued assignment or interview syllabus. A small end-to-end data project can help demonstrate readiness more clearly than a long list of course names. Choose a dataset you are permitted to use, explain the question you wanted to investigate and show how you checked the data before drawing conclusions.
For data cleaning, describe issues such as missing values, duplicate records or inconsistent formats and explain the decisions you made. Do not simply state that you cleaned a dataset without showing what changed and why. Clear notes about assumptions make a project easier to review and help distinguish deliberate choices from accidental changes to the information.
For model work, explain the target, the baseline and how performance was evaluated. Be ready to discuss limitations, including situations in which a result may not generalise. You do not need to pretend that a learning project is a production system. An accurate explanation of a modest project is more useful than unsupported claims about advanced artificial intelligence.
Web scraping and responsible data handling
Because web scraping is named in the description, applicants may want to explain any legitimate data-collection experience they have. Use sources that permit the intended access and respect site rules, privacy and rate limits. Do not bypass access controls or collect private information simply to create a portfolio sample. These are responsible preparation principles, not a claim that the employer has assigned a particular scraping project.
A useful demonstration could explain how publicly available information was collected, how errors were handled and how the resulting dataset was documented. If the example was created for learning, label it that way. Keep credentials, personal records and restricted information out of public repositories. Before sharing a previous work project, ensure you have permission to disclose the material.
What to include in an application
A focused resume can identify your Python or R experience, relevant data projects and the specific tasks you completed yourself. If you worked in a group, make your own contribution clear. Include a portfolio or repository link only if it is accessible, organised and safe to share. A short explanation of the project's purpose will help a reviewer understand why it is relevant.
Applicants can prepare concise answers about their availability, interests and learning goals. The listing does not publish a guaranteed coding-test format or interview schedule, so avoid paying for preparation marketed as an official test for this exact role unless the employer has verified it. Follow only the instructions supplied through the genuine application process.
Application steps and deadline
- Open the Kukbit SL Data Science internship page linked below.
- Confirm the employer, work-from-home arrangement, stipend and two-month duration.
- Read the current eligibility and registration deadline on Unstop.
- Use the platform's Apply option and complete any necessary account sign-in.
- Provide accurate application details and any documents requested by the employer.
- Submit before the registration deadline and retain the acknowledgement.
The registration cutoff is earlier than the shortlisting-round end time shown in the underlying listing. To avoid confusion, plan to submit well before 4:16 PM IST on 8 October 2026, rather than waiting for the later round time. If Unstop changes the deadline, the current registration control on the original page should be checked again before submission.
Questions to resolve before accepting
Ask the employer about the precise start date, supervision, expected daily availability, project scope, offered stipend and payment schedule. It is also reasonable to clarify how completion and recommendation letters are issued. These details are not fully explained in the advertisement, and a written offer is the right place to confirm them before committing two months of work.
This article reports an employer listing on Unstop; it is not an independent guarantee about the employer's working conditions or selection outcome. Unstop states that applicants should report recruitment fee requests. Jobsii does not collect application payments or promise an internship offer. Use the original platform to apply and verify unexpected requests before sharing sensitive information.
View Kukbit SL's Data Science internship and apply on Unstop.
