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The Algorithmic Trading Senior Manager at FanDuel will lead the algorithmic trading team, focusing on developing automated trading strategies and optimizing capital utilization while managing risk and team operations.

Lead and mentor multiple engineering teams, focusing on empowering their delivery of mobile and front-end applications by ensuring effective tools and capabilities.

Lead product strategy and execution for Payments supporting the Predictions market. Own end-to-end payments (deposits, withdrawals, wallet), partner cross-functionally with engineering, finance, compliance, and operations, drive metrics and experimentation, ensure regulatory controls and auditability, and deliver scalable, reliable payment solutions.

Manage qualitative and quantitative custom research (survey design, sampling, data analysis, reporting) across FanDuel verticals. Synthesize syndicated and internal data, build trackers, partner with vendors and cross-functional stakeholders, and present actionable insights to inform marketing and commercial decisions.

Partner with L&D leadership to design, deliver, and improve enterprise learning programs. Manage program coordination, LMS operations, vendor/stakeholder coordination, reporting, and facilitation across virtual, in-person, hybrid, and eLearning formats. Drive process improvements, learner engagement, and data-driven recommendations.

Lead development and execution of marketing campaign strategies for FanDuel Sportsbook. Translate commercial objectives and insights into creative briefs, coordinate agencies and internal teams, own planning and execution of major campaigns, define KPIs, deliver post-campaign analysis, and drive continuous improvement and product integrations to optimize campaign effectiveness and customer experience.

Lead and manage a data science team building production-ready machine learning models to predict sports outcomes, price markets, create dashboards, and drive insights. Collaborate with product, risk, and trading teams; oversee data collection and cleaning; apply Bayesian methods; troubleshoot code; present to senior leaders; hire and develop talent; improve processes and automation.

Lead and mentor a team of data engineers to design, build, and maintain scalable data pipelines, platforms, and data products. Provide technical oversight, code and architecture reviews, cross-functional partnership with product and analytics teams, and drive operational reliability, data quality, governance, and continuous improvement.

Design, build, and maintain scalable ETL/ELT, batch and streaming data pipelines and platforms. Create reusable data models (dbt/Spark), ensure data quality and observability, collaborate with data scientists, analysts, and product teams, and mentor junior engineers while advancing data platform architecture and tooling.

Lead and mentor a team of data engineers to design, build, and maintain scalable data pipelines, platforms, and products. Provide technical oversight, review architecture and code, collaborate with product and data science stakeholders, ensure operational stability, data quality, governance, and drive continuous improvement.