Roles and responsibilities:
The Lazada Algorithm team is a young and enthusiastic team comprising of experienced data scientists and machine learning engineers. Leveraging data-driven insights and Alibaba's Research & Development platform, we provide intelligent recommendations and furnish business insights to tackle complex real-world business challenges.
As a Data Scientist at Lazada, you will apply different techniques such as deep learning, reinforcement learning, natural language processing and operational research to drive and equip Lazada with the right strategies to best position ourselves in the market and to enhance platform growth. You will work alongside various teams to understand their different strategies and requirements.
The ideal candidate will be very hands-on and will dive deep into the root causes of any issues and blockers. You will be responsible for building algorithms specific to the domain that you will be assigned to. There are currently 4 domains under Lazada Data Science – of which you will be assigned to one:
1) Assortment Growth: including campaign product selection, pricing, product tagging, matching and recommendation, etc. to boost seller and platform growth.
2) Promotion: including the design, development and optimization of promotion voucher scheme generation, promotion recommendation, promotion distribution, performance prediction and analysis.
3) Logistics: including smart routing, parcel volume prediction, smart manpower arrangement, and deeply responsible for algorithm optimizations for business scenarios (first mile, last mile, and long haul)
4) Seller Growth: including seller profiling and selling opportunities mining for seller growth, product listing, category prediction, attributes mining and predictions, product knowledge graph, review mining, sentiment analysis, Southeast Asia natural language processing, etc.
Other activities which you will be involved in include:
1) Keeping up to date with the state-of-the-art research and integrated applications on deep learning, recommendation, optimization, reinforcement learning, transferring learning, amongst others.
2) Continually improving algorithms to adapt to evolving business requirements, and ensuring high efficiency, scalability as well as good coding style.
3) Regular communication across teams/functions and updates to relevant stakeholders on progress of projects.
4) Exploring possible new and better innovations by writing patents and top-level papers.