• Design, code, and test AI agents that support autonomous decision-making in marketing campaigns, customer acquisition, and portfolio optimization. • Apply hands-on coding knowledge to create agentic AI techniques to solve complex business problems in consumer lending. • Conduct exploratory data analysis and develop predictive models using credit bureau data and other consumer datasets. • Prototype and improve on intelligent agent behaviors using Python, Spark and modern agentic frameworks. • Support the deployment and monitoring of AI models in production environments, ensuring performance, reliability and compliance. • Collaborate with company partners to translate our requirements into scalable AI solutions. • Contribute to the development of new analytical products and services that enhance marketing effectiveness and customer engagement.

• Develop robust data pipelines for acquiring, cleaning, and preprocessing large-scale datasets from various sources. • Conduct comprehensive exploratory data analysis to uncover patterns, trends, and insights within the data. • Design, develop, and deploy predictive models using advanced machine learning algorithms and techniques. • Build scalable and efficient software solutions for deploying machine learning models into production environments. • Establish monitoring mechanisms to track the performance of deployed models and identify opportunities for improvement. • Collaborate closely with cross-functional teams including data engineers, software developers, and business stakeholders.

• Lead the development and deployment of enterprise-ready Generative AI solutions • Apply generative AI and automation technologies to solve real-world business challenges • Collaborate with cross-functional teams to ensure successful implementation • Drive innovation and excellence within the engineering team

• 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.