Data Science Intern

Mobile Premier League (MPL) is the world's leading mobile eSports platform that allows users to participate in free as well as paid competitions across 60+ games in multiple categories, including fantasy sports, sports games, puzzle, casual and board games. Founded in 2018, MPL hosts hundreds of millions of tournaments a month and is trusted by over 90 million registered users across Asia, Europe, and North America.

To learn more about MPL, please visit www.mpl.live

About the role

You will be responsible for owning and executing Data Analytics solutions, which include, handling large datasets, exploratory data analysis, model development, and solution deployment in real-time. We value first principles thinking, hence, strong logical reasoning, mathematical depth, and knowledge of data structures and CS algorithms are important for the role.

Here’s what you will do -

• Research and propose innovative statistical/ML models to address the requirements
• Target and formulate various problems in terms of key business metrics and measure the impact of the built ML techniques
• Analyze data for trends and patterns, and interpret data with a clear objective in mind
• Develop, deploy and maintain ML/DL models in production or help develop a platform to facilitate the same
• Work closely with the Engineering team to take Data Science projects to production
• Communicate progress/findings to relevant stakeholders
• Keep updated with emerging technologies in AI/ML

What are we looking for - 

• Experience with SQL, and Python (Required). Experience with PySpark, AWS, and Rest/Fast APIs (Preferred)
• Experience in Machine Learning, Deep Learning, and Engineering aspects of ML model deployment (Required). Has or pursuing Bachelor’s/Master’s/PhD degree in Computer Science, Statistics, Applied Mathematics, or related discipline (Preferred)
• Familiarity with statistical methods such as hypothesis testing, forecasting, time series analysis, etc (Required). Experience in working on optimization problems - linear/nonlinear programming, (meta)heuristics for solving optimization problems (Preferred)
• Most importantly, an inquisitive mind, an ability for self-learning and abstraction along with a risk appetite for experimentation and failure.
• Proficiency in data analysis, mathematics/probability, and statistical analysis (Required). Past Data Science project experience (Preferred)
• Should be comfortable multitasking and working in a dynamic, fast-paced environment
• Effective communication of DS requirements and dissemination of right knowledge to relevant stakeholders