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.
About the Role
The Research department is at the core of Riskified’s solution, responsible for our chargeback guarantee product and focused on bringing value to Riskified through the development of algorithms and analytical solutions. 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 ML Algorithm team is responsible for the full life-cycle of model development in Riskified. This includes defining, researching and implementing our traditional machine learning process in a fully automated fashion to enable fast and effortless model training and deployment. Additionally, the team is focused on automating increasingly complex aspects of the model configuration – dynamically setting thresholds to respond to population changes and segmenting the population in a smart fashion to improve the accuracy of each model. To enable this, we harness the latest MLOps architectures and require solid statistical foundations to ensure the quality of our products.
Our vision is to automate much of our internal data science and analytics work through advanced algorithm and machine learning engineering, pushing Riskified’s world-class solutions to the next stage.
We are looking for a full-stack Data Scientist / ML Engineer, with a strong background both in statistical modelling and writing production code. Experience with MLOps methodologies is a big advantage.
What You'll Be Doing
- Design, optimize and automate the full life-cycle of models in Riskified – Training, Deployment, Monitoring
- Collaborate with various teams within Riskified to enhance processes and expedite model development life-cycles
- Design algorithms to optimize models’ configurations for multiple merchants and under several various constraints including dynamic population changes
- Optimize, automate and monitor the whole model training and deployment process
- At least 2 years of experience as a Data Scientist/ML engineer in the industry
- M.Sc/Ph.D. in exact sciences/engineering disciplines
- Ability to write clean and concise code, ideally in R or Python
- Experienced with data science best-practices
- Solid understanding of statistics and applied mathematics
- Creative thinker with a proven ability to innovate through data exploration and application of advanced solutions
- Dedication and persistence when it comes to monitoring and improving performance after deployment
- Good communication skills, ability to clearly explain complex concepts
- Experience writing production code – Advantage
- Experience using Spark/Docker/Kubernetes and CI/CD – Advantage
- Experience with Bayesian Optimization / Control systems – Advantage
Life at Riskified
We are a fast-growing and dynamic startup with 500+ team members between our offices in Tel Aviv, New York City, and Shanghai. We value collaboration and innovative thinking. We’re looking for bright, driven, and passionate people to grow with us.
Some Tel Aviv Benefits & Perks:
- Stock options for all employees
- Keren Hishtalmut and pension
- Extra time off for parents and caregivers
- Commuter and parking benefits
- Team events, fully-stocked kitchen, lunch stipend, yoga, pilates, basketball, soccer, and more
To apply for this job please visit grnh.se.