Skimlinks is the world's leading content-to-commerce platform, helping publishers monetize their commerce-related content, and retailers & brands find people who want to buy their products.
Skimlinks helps publishers earn an incremental revenue stream by automating the process of earning through affiliate marketing. By aggregating 24,000 merchant affiliate programs from over 55 affiliate networks, 4.5 million websites are able to earn commissions and gain an understanding of the shopping behaviours of their audience. Clients include Conde Nast, Huffington Post, Business Insider, Buzzfeed and Refinery29.
Skimlinks then uses the data collected from this affiliate service to create the world's largest data co-op focused on shopping intent data. With data from 1 billion shoppers on the products they are reading about, clicking on, and buying, Skimlinks helps retailers and brands target and personalize based on these shopping behaviours. This means we have one of the world’s largest sets of AI training data around shopping intent, which attracts some of the greatest people to work at Skimlinks and build something truly great and impactful.
Our mission is to help publishers be rewarded properly for the role their content plays in creating shopping intent, providing a much needed funding model for the internet. We are also driven by creating a culture for our team that celebrates learning and growth, while caring for each other.
About the role
The Skimlinks data team sees roughly a terabyte of data a month across about a billion unique sessions. We estimate this is between 3-4% of events (page impressions/clicks) on the internet. We are looking for a talented engineer to join our Audiences team in London and develop algorithms and infrastructure for our machine learning and big data products. If you love working with big data and get a kick out of developing algorithms and systems that process terabytes of data we would love to hear from you. Day to day you will be:
- Developing systems to process and aggregate truly massive data sets
- Building classification frameworks
- Designing our core data architecture
- Building & scaling up machine-learning/deep-learning algorithms
- You will help define our development environment, and communicate the best development practices within the organisation (i.e. code reviews, testing, etc).
- You will share your knowledge across the business and mentor others in your areas of deep technical expertise
Skimlinks takes the approach that if you are a smart experienced programmer with an appetite for learning you do not need to have specific experience in the tools/stack we are working in. You should have deep experience in at least one area of software engineering, big data, machine learning or deep learning and a desire to learn the rest.
- You should have deep experience building enterprise-grade software working in a major language such as; Python, Java, C#, F#, Scala or C++ .
- You should have a passion for evolving projects and innovating new products, in an environment with lightweight agile processes
- You write clean maintainable code; you pay attention to details and edge cases
- Have worked with Spark
- Practical deep learning experience
- Machine learning
- Data science fundamentals
- Experience building systems that efficiently scale with very large data volumes
A flavour of our Technology Stack: