• Ideate, develop and improve machine learning and statistical models that drive Swish’s core algorithms for producing state-of-the-art sports betting products. • Develop contextualized feature sets using sports specific domain knowledge. • Contribute to all stages of model development, from creating proof-of-concepts and beta testing, to partnering with data engineering and product teams to deploy new models. • Strive to constantly improve model performance using insights from rigorous offline and online experimentation. • Analyze results and outputs to assess model performance and identify model weaknesses for directing development efforts. • Adhere to software engineering best practices and contribute to shared code repositories. • Document modeling work and present to stakeholders and other technical and non-technical partners.

• Ensuring the statistical rigor and validity of our products • Developing new techniques to bring value to our users • Advocating for technically-sophisticated personas in our product development process • Raising the bar for data-driven decision making within our products

• Design, develop, and implement advanced deep learning models, including transformers, CNNs, and LSTMs, to address complex fraud and risk challenges. • Build and optimize models using a variety of input data types, including tabular data, natural language, point clouds, and images. • Take ownership of assigned tasks, executing technical and functional activities to support project goals with minimal supervision. • Participate in all stages of the machine learning lifecycle: data exploration, feature engineering, model training, evaluation, and deployment. • Collaborate effectively across teams, sharing knowledge and learning from diverse perspectives to drive results. • Make routine technical decisions and contribute to functional objectives through productive and proactive engagement. • Stay current with advancements in AI and machine learning, applying innovative approaches to real-world problems. • Communicate results and insights clearly to both technical and non-technical audiences.

• Design, develop, and implement advanced deep learning models, including transformers, CNNs/RNNs, and graph learning algorithms, to address complex fraud and risk challenges. • Build and optimize models using a variety of input data types, including tabular data, natural language, point clouds, and images. • Lead the end-to-end machine learning lifecycle: data exploration, feature engineering, model training, evaluation, deployment, and monitoring in production environments. • Take ownership of project outcomes, data quality, and delivery timelines; proactively escalate issues and work collaboratively to resolve challenges. • Mentor and share knowledge with peers and junior data scientists, fostering a culture of experimentation, rapid iteration, and continuous learning. • Collaborate cross-functionally with Product, Engineering, and Risk teams to define data requirements and drive insights that guide strategic decisions. • Conduct in-depth research to explore new data sources and develop novel algorithms that advance the state of the art in fraud detection. • Present findings and recommendations to technical and executive stakeholders with clarity and influence. • Stay current with advancements in AI and machine learning, applying innovative approaches to real-world problems. • Model Socure’s embedded leadership competencies: continuous learning, effective communication, accountability, team development, decision making, and managing change.