• Design and develop algorithms and models to optimize Search relevance, retrieval, and ranking • Experiment design: ensure we run experiments efficiently • Develop and share key strategic insights through data analysis • Build scalable metrics and dashboards to track Search performance • Partner with cross-functional teams to support product development efforts • Care deeply about data quality

• Develop models and data-driven solutions that enhance the Sportsbook experience for our users • Create statistical and machine learning models and integrate them into data science applications. • Collect and engineer sports data assets to assist in model development. • Implement the sports models and pricing engines in Python. • Create automatic tests to ensure model and pricing engine accuracy. • Test data flows and model integration in a larger business context. • Coach and support more junior data scientists within the team.

• Develop Data Science solutions with a predictive and prescriptive focus, involving machine learning models (supervised and unsupervised) and optimization techniques that meet the business's strategic needs; • Execute the stages of a DS solution lifecycle: from business understanding, study, feature engineering and variable preparation, to model building and performance validation, providing the necessary support to deploy developed solutions to production; • Design, together with business areas, the use and monitoring of models, ensuring that predictions and prescriptions generate real and recurring value; • Identify opportunities to apply Generative AI and NLP as accelerators or analytical complements in day-to-day solutions.

• Work within a data organization structured into specialized fronts: Data Engineering, Analytics, Data Intelligence (AI), DataOps, Architecture, and Governance, acting as the link between PMs and technical squads; • Partner directly with the PM/Data Product and DataOps leadership to ensure deliverables have clear scope, success metrics, and visibility across the organization; • Serve as Product Owner within the area, responsible for keeping the backlog healthy, ensuring requests are well defined and deliveries are traceable end-to-end; • Support the PM in prioritizing and refining the data roadmap based on business impact and squad capacity; • Ensure intake requests are well defined before entering the backlog, with acceptance criteria, mapped data sources, and clear owners; • Create and maintain epics and tasks in Monday.com Dev, ensuring traceability between stakeholder requests and technical delivery; • Facilitate refinement ceremonies with squads, even in a model without formal sprints; • Act as the primary guardian of the area’s operational metrics: average cycle time, on-time delivery rate, backlog aging, post-delivery NPS, and rework rate; • Support DataOps leadership with visibility into capacity, bottlenecks, and SLAs per front; • Build and maintain delivery tracking dashboards so any stakeholder can understand the status of their requests; • Conduct periodic metric reviews with the PM and front-line leaders; • Proactively communicate progress, impediments, and deadline changes; • Support the PM in roadmap review ceremonies and monthly retrospectives; • Ensure delivered data products have minimum documentation, validated acceptance criteria, and identified business owners; • Identify opportunities to reuse analytical assets and propose process improvements to the PM.