Data Science Trainee / Data Analyst Paid Trainee
Summary
Entry-level paid trainee in Bengaluru: collect, clean and analyze datasets using Python/Pandas and SQL, build reports and dashboards in Excel, Power BI or Tableau, apply basic statistics, and assist with introductory machine learning and Generative AI tasks. Freshers and graduates from any background may apply.
- Collect, clean, transform, and analyze datasets to identify meaningful patterns.
- Use Python and Pandas for data manipulation and exploratory data analysis.
- Write SQL queries to retrieve, filter, join, and summarize data.
- Prepare reports, charts, and dashboards using Power BI, Tableau, or Excel.
- Apply basic statistical techniques to interpret data and support decision-making.
- Assist with data preprocessing, feature selection, and introductory machine learning tasks.
- Work on supervised projects, case studies, and analytical problem-solving assignments.
- Document findings and communicate insights clearly.
- Collaborate with team members on data-related tasks and project deliverables.
- Explore applications of Artificial Intelligence and Generative AI in analytics workflows.
Required Skills
- Basic understanding of analytical and logical problem-solving.
- Interest in Python programming and Data Analytics.
- Familiarity with SQL, Excel, or databases is an advantage.
- Understanding of mathematics, statistics, or data visualization is beneficial.
- Willingness to learn Machine Learning and emerging AI technologies.
- Good communication skills and a proactive attitude.
Preferred Educational Background
Technologies and Tools
Career Development
Career Opportunities
- Data Analyst
- Junior Data Analyst
- Data Science Associate
- Reporting Analyst
- Business Intelligence Analyst
- Junior Machine Learning Associate
How to Apply
Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.