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Data scientist job vacancies at MPL; know the hiring process

D2C Admin

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Data scientist job vacancies at MPL; know the hiring process

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With 60+ games played by over 81 million users in India, Indonesia, and the USA, Mobile Premier League (MPL) is a juggernaut that solves a varied set of AI/ML problems in the gaming industry with an abundance of data sets. It is one of India’s largest esports and mobile gaming platforms. Since its launch in 2018, it has done a great job in engaging users.

MPL has successfully launched an esports arena for titles such as World Cricket Championship, Chess, and Pool. It has various gaming studios and developers as partners to publish its games on its platform. Talking of its success, major credit goes to its data scientist team.

Here are some of the key initiatives of the Data Scientist team at MPL:

Game design: Learn from historic gameplays and tournaments to design new game levels and tournament formats.

Fair play for users: Real-time cheating and financial fraud detection algorithms ensure that the MPL gaming experience is fair.

Customer acquisition: Machine Learning(ML) models ensure they can acquire and retain customers with maximum return on their investment.

Recommendation system: Learn player preferences, skill levels, recommend games and lobbies to improve player skill and their chances of winning.

These initiatives are made successful by a team of data scientists at MPL. To ameliorate the team quality, MPL is hiring senior data scientists across various experience levels, including data scientists, senior data scientists, staff data scientists, etc.

Data Scientist hiring process at MPL

In the last year, MPL’s data science team has grown from zero to 16 members. Today, the company has a central data science team that works across different business verticals. The team has a mix of data scientists, data analysts, ML engineers, etc.

The data science team mainly deals with data analytics, data products, and data engineering. Ingenuity and freedom are the two words that best describe a data scientist at MPL. Candidates will get an opportunity to enhance not only the gaming experience but also the research work in the field.

Interview rounds:

MPL usually has three to four rounds after the screening of the CV.

Understanding the candidate:

They try to understand the candidate’s background better and check for any of their relevant open positions in data science.
They answer candidates’ queries regarding job profiles and the company.

Business knowledge:

They check the candidate's ability to break down a business problem into a data science problem.
Here, understanding business metrics is also essential.

Technical skill set:

If you are looking for required technical skills, then you must have the following skills:

Mathematical thinking: Statistics, mathematical modeling
Algorithms: CS, ML, data science fundamentals
Programming: OOPS, Python, SQL
System Design: ML Ops, Spark, AWS

Soft skills evaluation:

The company evaluates candidates on soft skills like people management, learning flexibility, and teamwork skills.

Expectations after selection:

The selected candidate will be responsible for owning and executing data science solutions. The candidate should be able to handle large sets of data and perform data analysis. The candidate should be able to break down complex business needs into tractable data science problems, identify and define new avenues where data science can add business value. Shubham Malhotra, the co-founder at MPL, says, “We are seeking exceptional data scientists to join our team so that our solutions are publishable and form the golden standard in the gaming industry.” Interested candidates can note the founder's thoughts and practice to enhance the relevant skills.

Important tips to ace the selection process

You should be able to demonstrate the ability to think from the first principles.
Be clear on expectations from the role. Ask relevant questions about the job profile, expectations, and work culture.
In the case study rounds, candidates often do not ask enough questions to get more clarity on the task at hand. Ask clarifying questions to understand the problem.
Focus more on the business impact of the projects.

The work culture at MPL

"Every data scientist at MPL co-owns a business metric with the respective business team and is responsible for improving the same,” said Shubham Malhotra, co-founder, MPL. MPL is highly data-driven, and outcomes are measured in terms of its ability to move a metric in the right direction. The best thing about being a data scientist at MPL is that you get to innovate and showcase your skills. The liberty that data scientists get is what makes it a good opportunity for aspirants.