Inmobi Freshers Recruitment As Data Science Intern For B.E/B.Tech/M.E/M.Tech


About the Company: InMobi Group’s mission is to power intelligent, mobile-first experiences for enterprises and consumers. Its businesses across advertising, marketing, data and content platforms are shaping consumer experience in a world of connected devices. InMobi Group has been recognized on both the 2018 and 2019 CNBC Disruptor 50 list and as one of Fast Company’s 2018 World’s Most Innovative Companies.

Company Name: Inmobi

Official Website:

Job Profile: Data Science Intern

Work Location: Bangalore

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Exp Needed 0-1 Years

Description: Excited by Big Data, Machine Learning and Predictive Software? Interested in creating new state-of-the-art solutions using Machine Learning and Data Mining techniques on Terabytes of Data?

Major responsibilities
• Use machine learning, data mining and statistical techniques to create new, scalable solutions for business problems
• Analyze and extract relevant information from large amounts of historical data to help automate and optimize key processes
• Design, develop and evaluate highly innovative models for predictive learning
• Establish scalable, efficient, automated processes for large scale data analyses model development, model validation and model implementation
• Research and implement novel machine learning and statistical approaches Basic Qualifications BASIC QUALIFICATIONS
• A Bachelors and/or Masters in Computer Science, Electronics or relevant field
• Experience in predictive modelling and analysis, predictive software development.
• Strong problem-solving ability
• Good skills with Java/Scala/Python (or similar scripting language)
• Experience in using R, Matlab, or any other statistical software
• Strong communication and data presentation skills Preferred Qualifications
• Experience handling gigabyte and terabyte size datasets
• Experience working with distributed systems and grid computing
• Knowledge of the latest and state of the art ML technology.
• Publications or presentations in recognized Machine Learning and Data Mining journals/conferences.

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