About the job
About Us
At Riskified, we empower businesses to drive e-commerce growth by mitigating risk. Our platform is trusted by some of the world's leading brands and publicly traded companies in their online operations. We provide guaranteed protection against chargebacks, combat fraud and policy abuse at scale, and enhance customer retention. Our AI-powered fraud and risk intelligence platform, developed by a dedicated team of e-commerce risk analysts, data scientists, and researchers, analyzes each interaction to deliver real-time decisions and insightful identity-based analytics. We're proud to partner with a diverse range of exceptional companies, including Acer, Gucci, Lorna Jane, and GoPro.
We excel in a collaborative environment that fosters innovation and product development that matters. The opportunity to contribute meaningfully drives our passion and purpose at Riskified, making it a fulfilling place to work.
About the Role
*** A third party will recruit, hire, and employ this position.
The Data Science department is integral to our company, adding value through the development of algorithms and production-grade analytical solutions. We utilize advanced techniques such as classification models, NLP, anomaly detection, and deep learning to maximize the value of data. As a Data Scientist, you will engage in the entire project development cycle, implementing end-to-end solutions. This role requires a strong foundation in quantitative and analytical skills, proficiency in statistical modeling and machine learning, and a technical aptitude paired with a passion for problem-solving and driving data-informed decision-making.
What You'll Be Doing
- Data Exploration and Preprocessing: Collect, clean, and transform large, complex data sets from diverse sources to ensure data quality and integrity for analysis.
- Statistical Analysis and Modeling: Utilize statistical methods and mathematical models to uncover patterns, trends, and relationships in data, while developing predictive models.
- Machine Learning: Create and deploy machine learning algorithms, including classification, regression, clustering, and deep learning techniques, to address business challenges and enhance processes.
- Feature Engineering: Identify relevant features from structured and unstructured data sources, designing new features to improve model performance.
