Data Science Internship in Bangalore

• Student pursuing B.Tech or M.Tech degree in engineering.
• Machine Learning Algorithms: Crystal clear understanding, coding, implementation, error analysis, model tuning knowledge on Linear Regression, Logistic Regression, SVM, shallow Neural Networks, clustering, Decision Trees, Random forest, XGBoost, Recommender Systems, ARIMA and Anomaly Detection. Feature selection, hyper parameters tuning, model selection and error analysis, boosting and ensemble methods.
• Strong with programming languages like Python and data processing using SQL or equivalent and ability to experiment with newer open-source tools.
• Good Knowledge in Data structure and algorithms.
• Good understanding of probability and ML would be added advantage.
• Job location – Bangalore (work from office).

What you’ll do?

• Partnering with internal business owners (product, marketing, edit, etc.) to understand needs and develop custom analysis to optimize for user engagement and retention.
• Good understanding of the underlying business and workings of cross functional teams for successful execution.
• Design and develop analyses based on business requirement needs and challenges.
• Leveraging statistical analysis on consumer research and data mining projects, including segmentation, clustering, factor analysis, multivariate regression, predictive modelling, etc.
• Providing statistical analysis on custom research projects and consult on A/B testing and other statistical analysis as needed. Other reports and custom analysis as required.
• Identify and use appropriate investigative and analytical technologies to interpret andverify results.
• Apply and learn a wide variety of tools and languages to achieve results.

About Company: Kaleidofin is a fintech platform that propels under-banked customers towards meeting their real-life goals by providing intuitive and tailored financial solutions. Our name ‘Kaleidofin’ is inspired by the power of financial solutions to enable beautiful possibilities of future life for each customer.