Dream Sports, the parent company of Dream11, is a prominent sports tech unicorn in India that houses a bunch of brands like Dream11, FanCode, and more, making it the Willy Wonka’s Chocolate Factory of sports engagement. Founded in 2008 by Harsh Jain and Bhavit Sheth, Dream Sports is headquartered exclusively in Mumbai, affectionately dubbed “The Stadium” by its team.
At the forefront, its flagship fantasy sports platform, Dream11, accommodates a massive user base of over 190 million individuals. This platform offers users the exciting opportunity to participate in fantasy versions of cricket, hockey, football, kabaddi, handball, basketball, volleyball, rugby, futsal, American football, and baseball.
And for the seamless operation of Dream Sports, data science has become a cornerstone in tackling fundamental challenges for platforms like Dream11, where user experience and engagement play pivotal roles.
AIM got in touch with Amit Sharma, chief technology officer at Dream Sports (Dream11) to know more about their AI, ML operations, hiring strategy, work culture and more. Since 2016, Sharma has led the creation of the sports tech platform. Earlier, he spent over a decade developing complex distributed systems for major companies like Yahoo! and Netflix in California.
Dream Sports is currently looking out for a VP of Data Science in Mumbai to lead data science roadmap development, drive experimentation, propose ML solutions, mentor the team, and ensure goal alignment. Required skills include programming languages, data visualisation, machine learning, and strong interpersonal skills. Familiarity with technologies like Cloudfront, API Gateway, Python, MySQL, Kafka, Spark, and Redshift is essential.
Inside Dream11’s AI & Analytics Play
Aligned with its mission of enhancing the sports experience, Dream Sports operates akin to a “high-performing sports team” composed of “Coaches” (CXOs) and “Captains” (team leaders) who guide over 1000 “Sportans” (employees). That includes a skilled roster of engineers, business and data analysts, applied scientists and machine learning experts.
One of the primary hurdles that this team has successfully tackled through data science is the issue of personalisation, discovery, and fraud detection within its application. When it comes to user interaction, the app encompasses a multitude of features, like matches and contests, resulting in a notable mental demand. These elements are subject to change, creating challenges for users to fully grasp and decide on the most suitable choices. Dream11 was an early adopter of data science in its product development journey, even during when these technologies were relatively nascent within the tech ecosystem.
“In order to improve user experience and engagement, we have deployed over 100 AI and ML models to enable the contextual discovery of relevant features. We have personalised multiple user journeys throughout the app by analysing user cohorts and behaviours,” said Sharma.
Underpinning these offerings are sophisticated ML systems that process an extensive array of over 1000 features across numerous users. Appropriate models and data drifts have also been implemented to ensure the smooth functioning of daily operations. The team of data scientists has also developed robust systems for detecting fraud by incorporating knowledge graphs, extensive searches for similarities, and algorithms for recognising patterns. They consistently carry out tests and refine methods for personalisation. They use A/B testing to compare various user experiences and gauge their effects on user engagement.
Data scientists at Dream11 continually experiment with various personalisation strategies, employing A/B testing methodologies to compare distinct user experiences. This iterative approach helps in identifying the most effective strategies while quantifying their impact on user engagement.
Tech Stack
Dream Sports’ technological infrastructure is a blend of in-house tools and third-party solutions.
“While our tech infrastructure is a combination of in-house tools and third-party solutions, we strongly believe in developing in-house solutions to minimise costs, strengthen data privacy, and control the scalability of services without compromising the architecture,” said Sharma.
One of their in-house frameworks, known as FENCE (Fairplay Ensuring Network Chain Entity), is employed to identify and address Fairplay violations, ensuring fair competition for users.
Their primary distributed ML systems rely on Spark and Ray. Transformers are utilised for various sequential learning tasks and have demonstrated superior performance compared to other deep learning models on their datasets, which are awaiting large-scale implementation. “We are exploring applications of LLMs in the context of the Sports ecosystem and testing internal prototypes,” Sharma commented.
For forecasting and classical machine learning applications, they rely on a range of resources, including Scikit Learn, XGboost, Prophet, and Scipy. Additionally, for deep learning-based machine learning tasks, the team leverages the capabilities of Pytorch and Tensorflow, harnessing their power to create robust and advanced models.
On the front of app development, Dream11 became one of the few tech companies to fully migrate their platform to React Native, a UI software framework. Despite industry scepticism regarding the feasibility of complete React Native adoption due to its historically low success rates, Dream11 navigated and overcame the associated challenges to make this happen.
Interview Process
Dream11 places a strong emphasis on valuing skills when hiring, seeking top talent aligned with their goal of enhancing the sports experience, encapsulated by the acronym “DOPUT”: Data-Driven, Ownership, Performance, UserFirst, and Transparency.
When hiring data science professionals, the organisation prioritises cultural fit initially. Upon meeting this criterion, candidates undergo a customised hiring process that varies by role. For most data science applicants, this process includes an aptitude test, followed by progressive technical interviews covering areas such as R programming, ML, and practical mock projects. Domain-specific interviews led by team leaders provide a thorough evaluation of the candidate’s preferences and skills.
One of the common mistakes that candidates make while interviewing is sometimes they miss out on the basic foundations of ML, stats or experimentation which are extremely valued at the organisation.
“While building models is easy, making them useful is tougher. That’s where hands-on implementations become a force multiplier,” said the CTO. Prospective team members can expect a supportive work environment with access to extensive qualitative data, challenging machine learning tasks, advanced infrastructure, a motivated team, and the chance to contribute at a large scale.
Work Culture
Driven by its culture, the company fosters an open and transparent atmosphere. Certified as a Great Place to Work, Dream Sports adopts a hyper-experimentation approach known as “HEAL – Hypothesis, Experiment, Analysis, and Learning” allowing employees to experiment, embrace failure, swiftly learn, and create personalised user features.
Employees enjoy a range of special perks and benefits like ‘Learning Wallet‘, unlimited leaves, ESOP, insurance, mental wellness initiatives and more. The Learning Wallet supports diverse learning ambitions, allowing individuals to explore areas such as design or coding regardless of their primary expertise. The unlimited leave policy promotes a healthier work-life balance, including the ‘Unplugged’ feature—a unique seven-day work-free vacation opportunity. Additionally, employees enjoy fully-paid access to sports events, matches, and tournaments.
“Most importantly, besides several industry-first benefits, we offer access to the latest tech stack and prioritise building a thriving culture through various engagement activities,” concluded Sharma.
Check out their careers page here.
Read more: Data Science Hiring Process at Naukri.com
