Role Description As an Applied Researcher on the Data Science team, you will play a critical role in bringing new products to market. - Prototype innovative ideas, conduct experiments, and iterate quickly to develop data-driven solutions for complex business challenges. - Independently work on end-to-end research projects while actively collaborating with a team of data scientists. - Present and promote results to internal stakeholders and external clients and partners. - Support and improve existing data science products. - Analyze large, complex, and noisy datasets to extract actionable insights and support data-driven decision-making. - Design, implement and evaluate machine learning and statistical methods to perform exploratory data analysis, data storytelling and product development. - Design and execute A/B tests and experiments to optimize product performance and maximize revenue growth. - Pursue patents where applicable. - Contribute to thought leadership in the community with presentations and publications. Qualifications - Independent research experience is mandatory. - A Ph.D. in a quantitative field (e.g. machine learning, statistics, physical science, or quantitative social science) is preferred, or a M.S. in Machine Learning or statistics with at least 3 years of independent research. - Experience using real data to solve real problems. - Expertise in statistical modeling, machine learning, and fluency in the related technical tools. - Interest in applying machine learning to large datasets to solve business problems. - Knowledge of current large language models and generative AI tools and an interest in applying them to data products. - Excellent oral and written communication skills; comfort in a client-facing role. - A track record in contributing to publications and/or presentations in a quantitative field. - Intermediate programming skills. - You should be ready to learn and work with Python, Google Cloud Platform, Scikit-learn, TensorFlow, PyTorch, Spark, BigQuery and SQL.

• Partner with engineering to define the architecture and technical strategy for internal risk systems and tooling. • Partner with Risk, Engineering, Security, Compliance, Product, and Operations to define risk metrics, success criteria, and decision frameworks. • Analyze fraud, scam, and abuse patterns across crypto and fiat products. • Build and maintain reporting that tracks fraud loss, false positives, customer friction, operational efficiency, and control performance. • Help design and architect the data, instrumentation, and decisioning systems that power risk operations and fraud prevention workflows. • Evaluate and iterate on risk rules and controls using rigorous analysis. • Apply statistical methods and selective ML techniques where they materially improve decision quality, operational efficiency, or signal detection. • Conduct deep-dive investigations into incidents, trends, and emerging threats. • Communicate insights clearly and persuasively to senior stakeholders • Help establish data standards, analytical methods, and decision-making practices within the Risk team

• Partner with engineering to define the architecture and technical strategy for internal risk systems and tooling. • Partner with Risk, Engineering, Security, Compliance, Product, and Operations to define risk metrics, success criteria, and decision frameworks. • Analyze fraud, scam, and abuse patterns across crypto and fiat products. • Build and maintain reporting that tracks fraud loss, false positives, customer friction, operational efficiency, and control performance. • Help design and architect the data, instrumentation, and decisioning systems that power risk operations and fraud prevention workflows. • Evaluate and iterate on risk rules and controls using rigorous analysis. • Apply statistical methods and selective ML techniques where they materially improve decision quality, operational efficiency, or signal detection. • Conduct deep-dive investigations into incidents, trends, and emerging threats. • Communicate insights clearly and persuasively to senior stakeholders • Help establish data standards, analytical methods, and decision-making practices within the Risk team

• Own line setting strategy and projection accuracy for our fantasy markets, balancing coverage breadth and competitive line quality. • Lead and grow a high-performing team of data scientists and analysts focused on market creation and prop origination. • Oversee development and deployment of projection models and coverage expansion across major sports and esports (NBA, NFL, MLB, CS2, etc.). • Partner cross-functionally with Product, Engineering, Pricing, and BizOps to launch new markets and expand coverage. • Guide experimentation around line accuracy, prop selection, and coverage strategy to drive engagement and grow the market catalog. • Maintain and evolve frameworks for projection accuracy, coverage quality, and market health monitoring. • Ensure data integrity and modeling quality across all origination efforts.