Job Description

ABOUT US

ShopBack : Better Shopping, Every Day. The ShopBack Group is Asia-Pacific\xe2\x80\x99s leading shopping and rewards platform, serving over 30 million shoppers across ten markets. Growing from a team of six back in 2014 to over seven hundred today, ShopBackers across the region come together with a singular mission: to make shopping rewarding, delightful, and accessible for all. Joining forces with leading buy now, pay later (BNPL) player hoolah and with the launch of ShopBack Pay, the Group now offers shoppers a responsible and convenient payment option at checkout. More than half a billion shopping trips start with ShopBack each year. The Group powers over US$3.5 billion in annual sales for over 10,000 online and in-store merchant partners, across categories ranging from fashion, beauty, F&B, electronics, travel and more. If you are passionate about building and scaling up businesses in this fast-growing landscape, come and join our growing ShopBack team!

About the Team The Data team is responsible for driving the adoption of data solutions across the company. We focus on three main missions:

• ShopBack\'s Data Governance: Whole data lifecycle; collection, storage, manipulation, retention, disposal, as well as the respect of legal compliance and data quality

• Empower ShopBackers to make data driven decisions: Usage and manipulation of data to create metrics, insights and detect patterns and promote a data driven culture

• Deliver a smarter experience to User and Merchants: Deliver Machine Learning products to improve the shopping experience for both the user and the merchant.

Requirements

• Significant prior success as a Machine Learning Engineer working on challenging problems at scale
• 4-8 years of industrial ML experience, with expertise in modeling and statistical modeling
• Able to mentor junior members in the team on Deep Learning capabilities
• Have full stack experience in data collection, aggregation, analysis, visualization, productionisation, and monitoring of ML products - aka MLOps
• Ability to develop ML Products with good engineering practice and mindset
• Strong desire to solve tough problems with scientific rigour at scale
• An understanding of the value derived from getting results early and iterating
• Strong skills in Python, machine learning, and deep learning libraries (Pytorch and Tensorflow)
• Passion to answer Product/Engineering questions with data

Good-to-have

• Masters or PHD in quantitative fields such as Computer Science, Operations Research, Statistics, Econometrics or Mathematics