Data Scientist

About the role

About Us

Riskified empowers businesses to unleash ecommerce growth by taking risk off the table. Many of the world’s biggest brands and publicly traded companies selling online rely on Riskified for guaranteed protection against chargebacks, to fight fraud and policy abuse at scale, and to improve customer retention. Developed and managed by the largest team of ecommerce risk analysts, data scientists and researchers, Riskified’s AI-powered fraud and risk intelligence platform analyzes the individual behind each interaction to provide real-time decisions and robust identity-based insights.

Riskified is proud to work with incredible companies in virtually all industries including Booking.com, Acer, Gucci, Lorna Jane, GoPro, and many more.

We thrive in a collaborative work setting, alongside great people, to build and enhance products that matter. Abundant opportunities to create and contribute provide us with a sense of purpose that extends beyond ourselves, leaving a lasting impact. These sentiments capture why we choose Riskified every day.

About the Role

The Data Science department powers Riskified’s core value engine by building scalable, production-grade ML solutions that protect revenue and automate complex decisions. We leverage diverse analytical techniques - from advanced statistical modeling and graph theory to classic machine learning and deep learning - to extract maximum signal from complex datasets.

As a Data Scientist, you will own projects end-to-end: framing business problems, developing models, and deploying them to production. This role demands strong quantitative fundamentals, deep machine learning expertise, and a pragmatic problem-solving mindset to translate raw data into real business impact.

What You'll Be Doing

- End-to-End ML Development: Design, train, evaluate, and optimize scalable predictive models (classification, regression, clustering, deep learning) for complex business challenges.

- Data & Feature Engineering: Clean, transform, and extract high-value features from large-scale structured and unstructured data to continuously boost model performance.

- Collaboration & Production: Work within the Data Science team and alongside Data Engineers, Developers, and Product Managers to deploy models into production and translate complex insights into clear business actions.

- Innovation & GenAI: Continuously explore emerging ML techniques, state-of-the-art tools, and GenAI workflows to elevate the team's analytical capability.

Qualifications

- M.Sc. in Computer Science, Electrical Engineering, or a related quantitative field.

- 2+ years of industry experience developing and deploying machine learning models in production environments.

- Deep theoretical understanding and practical experience of ML algorithms on tabular and structured data.

- Proactive team player with a strong curiosity and enthusiasm for adopting modern GenAI developer tools.