Lead Data Scientist at MattelInc

The Opportunity:  We are seeking a Lead Data Scientist to drive advanced analytics and AI initiatives within our Product Quality Analytics organization. This role sits at the intersection of data science, product quality, and business strategy, and is responsible for leading high impact initiatives that transform complex data into scalable insights and intelligent systems. You will own the end-to-end lifecycle of data science solutions, from problem framing and modeling to deployment and adoption, while influencing stakeholders and mentoring team members. This is a highly visible role with the opportunity to shape how analytics and AI are embedded into quality decision making across the enterprise. What Your Impact Will Be:  Leadership and Strategy • Lead the design and execution of advanced analytics and machine learning initiatives aligned to product quality and consumer insights • Partner with key stakeholders to define high value business problems and translate them into scalable data science solutions • Act as a thought leader in AI and machine learning, driving adoption of modern techniques including generative AI, natural language processing, and predictive modelingModeling and Analytics • Develop and deploy models such as classification, forecasting, anomaly detection, natural language processing, and causal inference • Apply statistical techniques such as regression, time series analysis, and experimentation to solve complex business challenges • Build frameworks for early signal detection, quality risk prediction, and root cause analysisProductionization and Implementation • Collaborate with data engineering to productionize models using Google Cloud Platform tooling such as Agent Platform, Cloud Run, and Dataform • Contribute to scalable data products and pipelines that enable self service analytics • Ensure models are monitored, maintained, and continuously improved with an MLOps mindsetCollaboration and Cross Functional Partnership • Partner with data engineering, analytics, and business teams to align on priorities, data needs, and solution design • Work across teams to ensure data consistency, shared understanding of metrics, and scalable solutions • Bridge technical and non technical stakeholders by communicating clearly and driving adoption of data science solutions