Data Scientist

<h2><strong>About Us</strong></h2> <p>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 Acer, Gucci, Lorna Jane, GoPro, and <a href="https://www.riskified.com/customers/">many more</a>.</p> <p>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. </p> <p> </p> <h2><strong>About the Role</strong></h2> <p>The Data Science department plays a pivotal role in our company, generating value to Riskified by developing algorithms and analytical production-grade solutions. We leverage 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). As a Data Scientist, you will assume the classic data-science role of an end-to-end project development and implementation practitioner. Being part of the team requires a mix of hard quantitative and analytical skills, solid background in statistical modeling and machine learning, a technical data-savvy nature, along with a passion for problem-solving and a desire to drive data-driven decision-making.<strong><br></strong></p> <p> </p> <h2><strong>What You'll Be Doing</strong></h2> <ul> <li>Data Exploration and Preprocessing: Collect, clean, and transform large, complex data sets from various sources to ensure data quality and integrity for analysis</li> <li>Statistical Analysis and Modeling: Apply statistical methods and mathematical models to identify patterns, trends, and relationships in data sets, and develop predictive models</li> <li>Machine Learning: Develop and implement machine learning algorithms, such as classification, regression, clustering, and deep learning, to solve business problems and improve processes</li> <li>Feature Engineering: Extract relevant features from structured and unstructured data sources, and design and engineer new features to enhance model