ShopBack , the #1 rewards and discovery platform in Asia-Pacific, enables shoppers across the region to shop ‘The Smarter Way”. It is a one-stop rewards and discovery platform for users to earn cashback, while delivering performance-based marketing to merchants. First launched in Singapore in 2014, ShopBack has since expanded its reach to Malaysia, Indonesia, the Philippines, Thailand, Taiwan, Australia, and more recently, Vietnam and Korea. In Singapore, the company has also extended its service offering with ShopBack GO, an app-based rewards platform for in-store shopping, dining, and entertainment. We are a passionate team that wants to drive innovation and build a product that we love and are all proud of!
ShopBack partners with over 3,000 merchants including Taobao, Expedia, Shopee, ZALORA to reward its users with cashback across a wide range of categories including general merchandise, travel bookings, fashion, health and beauty, groceries, and food delivery. To date, US$100m in cashback has been awarded to our over 20 million users.
We have global ambitions and are up against international incumbents in a rapidly emerging field! We are expanding our existing team and are looking for passionate talent across APAC to be part of this exciting journey. If you are inspired to take up new challenges and leave a mark on the e-commerce landscape, then come and be part of our growing ShopBack Family!
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.
What you will be doing:
Understanding business objectives and developing models that help to achieve them, along with metrics to track their progress
Managing available resources such as hardware, data, and personnel so that deadlines are met
Analysing the ML algorithms that could be used to solve a given problem
Exploring and visualising data to gain an understanding of it, then identifying differences in data distribution that could affect performance when deploying the model in the real world
Verifying data quality, and/or ensuring it via data cleaning
Supervising the data acquisition process if more data is needed
Finding available datasets online that could be used for training
Defining the validation strategies, data augmentation pipelines and the preprocessing or feature engineering to be done on a given dataset
Training models and tuning their hyperparameter
Deploy model as containerisation through Kubernetes
Significant prior success as a Machine Learning Engineer working on challenging problems at scale
3+ years of industrial ML experience, with expertise in machine learning and statistical modeling
Have full stack experience in data collection, aggregation, analysis, visualisation, productionisation, and monitoring of ML products
Strong desire to solve tough problems with scientific rigor at scale
An understanding of the value derived from getting results early and iterating
Ph.D. or M.S. in a quantitative field such as Computer Science, Operations Research, Statistics, Econometrics or Mathematics
Strong skills in Python, Spark and machine python machine learning librariesFamiliar with Containerization and Kubernetes