The mission of the Marketplace Intelligence team is to build sustainable and scalable data products to promote the business and mission of Shopee marketplace by analyzing massive item and user related data, producing reliable predictive business insights and data-driven services, maximizing the effectiveness of marketing campaigns as well as providing personalized e-commerce experiences based on all-round item profiling and user profiling data and information.

Job Description:

• Work closely with the promotion product team, business partners, and internal stakeholders to identify marketplace growth challenges, uncover actionable insights, and deliver practical, scalable solutions based on large-scale user and product data.
• Drive the development and optimization of smart voucher dispatching strategies across various campaign scenarios by leveraging advanced deep learning, causal inference, and operations research techniques
• Collaborate with product and engineering teams to design and build end-to-end machine learning pipelines to power data-driven decision-making for promotions and campaign planning.
• Work on core solutions related to personalized recommendations, promotion and voucher configuration optimization, campaign budget planning, and product demand forecasting.
• Develop, iterate, and maintain machine learning models, including those based on deep learning, reinforcement learning, and multi-armed bandit frameworks, applied in areas such as recommendation, search, and ads ranking.
• Conduct rigorous online and offline evaluations, including A/B testing, to ensure model effectiveness and business impact.

Requirements:

• Master or PhD in Computer Science, Engineering, Mathematics, Statistics, Biostatistics or other fields related to data mining preferred.
• 2+ years of relevant industry experience in at least one programming language (e.g., Python, Golang, Scala) and Unix/Linux system and comfortable working with large datasets and conducting complex data analysis using SQL, Python or R.
• Knowledge in optimisation, classical machine learning (classification, regression, clustering, etc), deep learning, reinforcement learning.
• Experience in Tensorflow/Pytorch machine learning framework, distributed data processing framework (e.g., Hadoop, Spark) and conducting production environment A/B test.
• Practical project experience of uplift modeling, deep learning based recommendation systems (matching, pre-ranking, ranking, etc) in fields such as search, recommendation or ads , online and offline optimisation (multi-armed bandit, reinforcement learning) preferred.
• Good communication skills with demonstrated ability to deliver and explain technical content to stakeholders.
• Ability to deliver quality work under a tight timeline
• Teamwork mindset and ability to work closely with cross-functional teams.