Data Analytics - Internship

About the internship:1. Identify, analyse and interpret patterns from data sets using statistical methods

2. Acquiring data from internal as well as secondary data sources

3. Cleaning and filtering data, assessing data quality and create concise end product 4. Over time, you will be expected to contribute to the further development of our team by coaching less experienced colleaguesWho can apply:Only those candidates can apply who:are available for full time (in-office) internshipcan start the internship between 21st Sep'26 and 26th Oct'26are available for duration of 6 monthshave relevant skills and interests* Freshers looking for jobs may also applyStipend:INR₹ 30,000 /monthDeadline:[hidden]:59:59Other perks:Certificate, Flexible work hours, Informal dress code, 5 days a week, Free snacks & beverages, Job offerSkills required:Python, Data Analytics and MS-ExcelOther Requirements:1. A university degree from reputed Indian or International universities with a record of academic excellence. Xth & XIIth scores, 80% and above.2. An awareness and interest in economic, financial and general business concepts and terminology3. Excellent written and verbal command of English4. Strong problem-solving, analytical and quantitative skills5. A professional attitude and service orientation with the ability to work with London based international teamAbout Company:Galytix is a London-based technology company on its journey to becoming the smart data analytics platform of choice for financial services professionals globally. The platform gives influential decision makers a competitive edge by ingesting a large number of data sources, structuring and credentialling unstructured data, and running advanced analytics and machine learning models to derive decision insights - in a World's First Fully Integrated Solution.Additional information:Job offer: On successful conversion to a permanent employee, the candidate can expect a salary of ₹ 600000 /year

Responsibilities

1. Identify, analyse and interpret patterns from data sets using statistical methods

2. Acquiring data from internal as well as secondary data sources

3. Cleaning and filtering data, assessing data quality and create concise end product 4. Over time, you will be expected to contribute to the further development of our team by coaching less experienced colleagues