SLONIX Data Science Internship in Bangalore: ₹20,000–30,000 Monthly Stipend
SLONIX Solution Private Limited is advertising a Data Science Internship in Bangalore with a monthly stipend range of ₹20,000 to ₹30,000. The three-month assignment is hybrid, full-time and follows an evening-shift schedule across five working days. Seven openings are listed. This is a paid technical opportunity, but it should not be confused with a fully remote internship.
The complete listing requires applicants to be pursuing a relevant undergraduate programme in their final or pre-final year. Although the platform displays broader eligibility badges, candidates should follow the more specific requirement in the detailed description. Working knowledge of Python and common data science libraries is also expected.
Key Details and Application Closing Time
- Employer: SLONIX Solution Private Limited.
- Position: Data Science Internship.
- Location and arrangement: Bangalore, hybrid.
- Duration: Three months.
- Advertised stipend: ₹20,000–30,000 per month.
- Openings: Seven.
- Timing: Full-time, evening shift, five working days.
- Registration closes: 15 October 2026 at 12:00 a.m. IST.
The closing time comes from the live listing's calendar information. Midnight at 12:00 a.m. marks the beginning of 15 October, not the end of that date. Plan to submit before that timestamp; do not wait until the evening of 15 October. Applicants should check the current Unstop page for any subsequent change before applying.
What the Internship Covers
The responsibilities span several stages of an analytical workflow. Interns will collect, clean, transform and validate structured and unstructured datasets from relevant sources. They will conduct exploratory data analysis and communicate patterns, trends and anomalies rather than simply running tools without interpreting their output.
The role also includes supporting the development, testing and evaluation of statistical and machine learning models. Visualisations, dashboards and concise reports are expected to explain findings clearly. Working with team members to understand requirements, documenting processes and presenting recommendations are part of the assignment.
The source specifically highlights quality, reproducibility and data privacy. These points matter when handling datasets: analytical results should be explainable, processes should be documented, and sensitive information should not be exposed casually. The advertisement does not identify a particular client project, industry dataset or deployment platform, so applicants should not assume those details.
Who Can Apply?
The detailed requirement is a relevant undergraduate programme, with the applicant currently in the final or pre-final year. It does not name a compulsory college, specify a numerical marks cutoff or publish an age limit. It also does not say that every postgraduate candidate or already-graduated applicant qualifies merely because broad platform tags are visible.
Applicants need working knowledge of Python and libraries such as Pandas, NumPy and scikit-learn. Understanding statistics, data analysis and basic machine learning concepts is expected. The employer also asks for methodical problem-solving, the ability to interpret results and clear explanations of technical ideas.
Attention to detail, willingness to learn and collaboration in a hybrid environment complete the published requirements. There is no stated minimum number of years of paid professional experience. Candidates should nevertheless be able to demonstrate the listed skills through genuine academic, personal or other relevant work.
Hybrid Work, Evening Shift and Stipend
Bangalore is the advertised internship location, and the work type is hybrid. The source does not publish the number of office-attendance days or exact daily start and finish times. The five-day working week and evening-shift label are confirmed, but applicants should clarify the practical attendance and timing arrangements before accepting an offer.
The platform gives a minimum stipend of ₹20,000 and a maximum of ₹30,000 per month. It does not explain an incentive component or how a candidate's exact amount will be determined. Treat the figures as the advertised range, not a guarantee that every selected intern receives the maximum.
The internship is listed as full-time for three months. No permanent conversion, accommodation, relocation payment, certificate or letter of recommendation is promised in the verified details. The absence of those promises does not change the stated stipend; it means applicants should avoid filling gaps with assumptions.
How to Apply Through the Official Listing
- Open the SLONIX Data Science internship page on Unstop.
- Expand and read the complete Details, compensation, duration and work-timing sections.
- Check your undergraduate year, technical skills and availability for hybrid work in Bangalore.
- Use Quick Apply and follow the application's instructions with accurate information.
- Submit before 15 October 2026 at 12:00 a.m. IST and monitor Unstop for communication.
Preparing a Relevant Application
The following is editorial preparation advice, not an additional employer requirement. Select one small data project that shows cleaning, exploratory analysis and a clear conclusion. Explain the data source, missing-value handling, any train/test split, evaluation choice and what you learned. If your project includes a model, discuss its limitations rather than presenting a score without context.
Keep examples reproducible and use public or safely anonymised data. A concise README and understandable chart can communicate your approach better than a long list of library names. Be honest about your current experience, and make your final/pre-final-year status and Bangalore availability clear.
This opening suits students who want paid exposure to analytical work and can accommodate the hybrid evening-shift arrangement. The decisive checks are the detailed eligibility conditions, actual stipend offer and schedule agreed with the employer. Always return to the live listing before making an application decision.
