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Data Science Hiring Process At Mobile Premier League (MPL)

Data Science Hiring Process At Mobile Premier League (MPL)

  • MPL solves a diverse set of AI/ML problems specific to the gaming industry with one of the largest available datasets in the mobile gaming space in the world
Data Science Hiring Process At Mobile Premier League (MPL)

Mobile Premier League (MPL) is one of India’s largest esports and mobile gaming platforms, with 60+ games played by over 81 million users in India, Indonesia and the USA.

Since its launch in 2018, MPL has successfully launched an esports arena to host fortnightly tournaments across marquee esports titles such as Chess, World Cricket Championship and Pool. In addition, MPL has multiple gaming studios and developers as partners to publish their games on its platform.

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With a personnel strength of over 900 employees across the globe, MPL has offices in Bengaluru, Pune, Jakarta, Singapore and New York.

MPL solves a diverse set of AI/ML problems specific to the gaming industry with one of the largest available datasets in the mobile gaming space in the world. 

“This translates to an opportunity to do original research and build novel solutions that can set the gold standard in the gaming industry and create the best mobile gaming experience for billions of users across the globe. Only a handful of gaming companies across the world will be able to provide a similar ecosystem to solve problems at a global scale,” said Sai Srinivas, co-founder and CEO at MPL.

Here are some of the key initiatives driven by the data science team at MPL: 

  • Fair play enablement: Real-time cheating and financial fraud detection algorithms ensure that MPL provides a fair gameplay experience to all players 
  • Customer acquisition and lifecycle management: ML and optimisation models ensure they can acquire and retain customers with maximum return on their investment
  • Matchmaking:  Maximise in-game engagement by ensuring players of similar skill levels lay with each other
  • Personalisation/recommendation engines: Learn player preferences and skill levels and recommend games and lobbies to improve player skill and their chances of winning
  • Games/tournament design: Learn from historic gameplays and tournaments to design new game levels and tournament formats 

Now, MPL is looking to strengthen its data science team by hiring senior data scientists across various experience levels, including data scientists, senior data scientists, staff data scientists, principal data scientists, etc. 

Team Structure 

In the last year, MPL’s data science team has grown from scratch to 16 members. Today, the company operates a central data science team that works across different business verticals. The team includes a blend of data scientists, ML engineers and data analysts, following a flat structure consisting of individual contributors with varying experience levels, led by the head of data science. 

The data science team falls within the broader data organisation, which comprises data analytics, data products and data engineering. 

“For a project rising in any of the business pods, a team of individual contributors is put together that works closely with the product, engineering and ops team to ensure the desired business outcomes are achieved,” said Srinivas. 

Interview process 

Srinivas said once a CV has been screened for interviews, they typically have three to four rounds of discussion which focus on the following aspects depending on the seniority of the role being interviewed for: 

  • Explore:
    • Provide a download of MPL’s business model and the role of the data science team in enabling the business
    • Understand the candidate’s background better and check fitment for any of their open positions in data science 
    • Answers any questions the candidate may have
  • Business understanding:
    • Ability to understand and break down a business problem into a data science problem
    • Ability to understand business metrics and how data science solutions can impact them
  • Technical skills: At this stage, MPL evaluates a candidate on four technical parameters:
    • Mathematical thinking – statistics, mathematical modelling
    • Algorithms – CS, ML, data science fundamentals
    • Programming – OOPS, Python, SQL
    • System Design – ML Ops, Spark, AWS
  • Cultural fitment: Here, the company evaluates a candidate on their soft skills such as working in a team, people management, willingness to learn and adapt, etc.

Expectations

“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. This is a highly impactful role where your work will help the team create the best gaming platform in the world,” said Shubham Malhotra, co-founder at MPL.

The selected candidate will be responsible for owning and executing data science solutions, including handling large datasets, exploratory data analysis, model development and solution deployment in real-time. The company values first principles thinking; hence, strong logical reasoning, mathematical depth, knowledge of data structure and computer science algorithms are important for the role. 

See Also

An ideal candidate interviewing for data science roles at MPL 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, determine a long term development roadmap for the data science team, manage a small team and lead projects end-to-end, maintain visibility on work and progress across different stakeholders and address any process concerns.

Dos and don’ts 

According to MPL, the most common mistakes made by a candidate when interviewing/applying for a data science role include: 

  • Not focusing on the business impact of their projects.
  • Focusing more on the breadth of the problems that they have worked on rather than depth
  • Should be able to demonstrate the ability to think from first principles since many of our problems are non-conventional in the AI/ML industry
  • Don’t hesitate to ask questions about the work done at MPL – being clear on expectations from the role is good. It helps both the interviewer and the candidate in finding the right fit for themselves.
  • In the case study rounds, candidates often do not ask enough questions to get more clarity on the task at hand. Understanding the problem in detail is very important before taking a crack at the solution. Also, from an interviewer’s point of view, good questions from the candidate is an equally important signal as good solutions.

Work culture 

“Every data scientist at MPL co-owns a business metric with the respective business team and is responsible for improving the same,” said Malhotra. 

In a nutshell, MPL is highly data-driven, and all outcomes are measured in terms of its ability to move a metric in the right direction. “With over a million daily active users, it’s fascinating how much impact the slightest of improvements in ML models can bring to the table. The ownership and freedom enjoyed by data scientists to innovate and the ability to create measurable impact to the business makes MPL a very exciting workplace for a data scientist,” added Srinivas. 

Conclusion 

“Imagine a future where esports tournaments such as chess, pool or shooting can be held globally with millions of concurrent users on a single app!” said Naman Jhawar, SVP – strategy and operations at MPL. 

At MPL, a data science career will not only allow the candidates to create the best mobile gaming experience for billions of users across the world but will also give them a chance to conduct original research in an upcoming global industry.

Apply for the role of senior data scientist here

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