Riskified is the AI platform powering the eCommerce revolution. We use cutting-edge technology, machine-learning algorithms, and behavioral analytics to identify legitimate customers and keep them moving toward checkout. Merchants use Riskified to increase revenue, prevent fraud, and eliminate customer friction. Riskified has reviewed hundreds of millions of transactions and approved billions of dollars of revenue for merchants across virtually all industries, including Wish, Prada, Aldo, Finish Line, and many more. We're privately funded and VC backed, and our recent Series E round raised $165 million with a valuation in excess of $1 billion. Check out the Riskified Technology Blog for a deeper dive into our R&D work.
Riskified is the world's leading eCommerce fraud-prevention company. We use cutting-edge technology, machine learning algorithms, and behavioural analytics to outsmart eCommerce fraud and help our merchants grow. Riskified has reviewed hundreds of millions of transactions and approved billions of dollars of revenue for merchants across virtually all industries, including a number of Fortune 500 companies.
About The Role
The Research and Data Science department is focused on bringing value to Riskified through the development of algorithms and analytical solutions. As a data scientist in the research department, you will be a valuable member of an innovative and technical data science team working on various problems. The department uses a wide variety of advanced techniques and algorithms to provide maximum value from data in all shapes and sizes (such as classification models, NLP, anomaly detection, graph theory, deep learning, and more).
The Chargeback Algorithm team is responsible for the Research behind Riskified’s core product. The team uses versatile methods (such as classification, clustering, ensembles) to optimize the performance of our models, and develops flows to support operational model training / validation processes and monitoring. We work in big-data settings (e.g. spark, kafka, etc.) using state-of-the-art environments (databricks). We are responsible for the design, research and development of these tools, hence part of our work includes writing production code.
What You'll Be Doing
- Work to improve Riskified’s core product algorithm’s accuracy by implementing improvements to the ML flow
- Research & implement improvements in our our decision engine algorithms
- Work with Product & Dev teams to enhance processes and expedite model development life-cycles
- Mentor team members in a variety of Data Science projects
- 5+ years experience as a data scientist, applied researcher or similar in the industry
- M.Sc/Ph.D in exact sciences
- Experience in ML techniques & DS best practices
- Deep theoretical knowledge in ML algorithms, solid understanding of statistics and applied mathematics
- Extensive knowledge in classification problems of imbalanced data sets, including experience with boosting trees & ensembles
- Ability to write clean and concise code, ideally in R or Python
- Excellent communication skills with the ability to clearly explain complex concepts to business stakeholders
- Creative thinker with a proven ability to innovate through data exploration and application of non-trivial solutions
- Experience using Spark/Docker/Kubernetes and CI/CD - Advantage
- Experience with Bayesian Optimization / Control systems - Advantage