Data Analytics - Internship

About the internship:Selected intern's day-to-day responsibilities include:

1. Collecting, cleaning, and validating data from various sources.

2. Analyzing datasets using Excel, SQL, and Python to identify trends and patterns.

3. Writing Python scripts for data extraction, transformation, automation, and analysis tasks.

4. Supporting database management and data collection processes.

5. Collaborating with cross-functional teams to understand business requirements and information needs.

6. Ensuring data quality, accuracy and consistency across datasets.Who can apply:Only those candidates can apply who:are available for full time (in-office) internshipcan start the internship between 8th Oct'26 and 12th Nov'26are available for duration of 6 monthsare from Delhi onlyhave relevant skills and interests* Women wanting to start/restart their career can also apply.Stipend:INR₹ 15,000 /monthDeadline:[hidden]:59:59Other perks:Certificate, Letter of recommendation, Informal dress code, 5 days a weekSkills required:Python, SQL, Data Analytics, MongoDB, MS-Excel, Data Visualization, Advanced Excel, Power BI and Data CleaningOther Requirements:1. Only participants who have completed their studies or are in their last year of studies2. Candidates who only access work from office, no work from home is allowed3. Candidates should be available for minimum 6 months of internship and after successful completion will be offered a jobAbout Company:We are an ISO-certified social impact technology lab focused on the design and development of technology tools for the public good. Our team has worked in over 100 countries for international organizations like UNICEF, UNFPA, UNDP, World Bank, UN Women, Save the Children, and the World Food Programme.

Responsibilities

Selected intern's day-to-day responsibilities include:

1. Collecting, cleaning, and validating data from various sources.

2. Analyzing datasets using Excel, SQL, and Python to identify trends and patterns.

3. Writing Python scripts for data extraction, transformation, automation, and analysis tasks.

4. Supporting database management and data collection processes.

5. Collaborating with cross-functional teams to understand business requirements and information needs.

6. Ensuring data quality, accuracy and consistency across datasets.