• Drive advanced analytics, causal reasoning, and AI-powered decision intelligence across multiple use cases within our AI portfolio • Build production-grade systems that combine predictive, causal, and generative AI capabilities • Design and deploy systems that integrate experimentation, observational data, machine learning, and generative AI into real-time or near-real-time decision-making pipelines • Collaborate closely with data engineers, ML engineers, analysts, and platform teams • Develop and deploy machine learning models across use cases (forecasting, optimization, recommendation systems) • Apply statistical, predictive, and prescriptive modeling techniques to solve business problems • Build reusable modeling frameworks that can scale across multiple domains • Design and implement causal inference methods (e.g., uplift modeling, experiments, quasi-experimental methods) • Translate observational and experimental data into actionable business insights • Embed causal reasoning into decision systems that guide actions

• Drive advanced analytics, causal reasoning, and AI-powered decision intelligence across multiple use cases within our AI portfolio. • Work at the intersection of data science, machine learning, and GenAI, turning complex data into actionable insights, automated decisions, and intelligent workflows. • Design and deploy systems that integrate experimentation, observational data, machine learning, and generative AI into real-time or near-real-time decision-making pipelines. • Collaborate closely with data engineers, ML engineers, analysts, and platform teams, contributing to shared modeling standards and cross-functional AI architecture. • Develop and deploy machine learning models across use cases (forecasting, optimization, recommendation systems). • Apply statistical, predictive, and prescriptive modeling techniques to solve business problems. • Build reusable modeling frameworks that can scale across multiple domains. • Design and implement causal inference methods (e.g., uplift modeling, experiments, quasi-experimental methods). • Translate observational and experimental data into actionable business insights. • Embed causal reasoning into decision systems that guide actions (e.g., optimization, prioritization, trade-offs). • Integrate GenAI capabilities (e.g., LLMs, RAG pipelines, agent-based systems) into data science workflows. • Contribute to the development of intelligent agents and AI-assisted decision-making systems. • Combine structured data models with unstructured data and GenAI outputs.

• Drive advanced analytics, causal reasoning, and AI-powered decision intelligence across multiple use cases • Build production-grade systems that combine predictive, causal, and generative AI capabilities • Turn complex data into actionable insights, automated decisions, and intelligent workflows • Design and deploy systems that integrate experimentation, observational data, machine learning, and generative AI • Collaborate closely with data engineers, ML engineers, analysts, and platform teams

• Own the end-to-end data science lifecycle for moderately complex models and significant project components — spanning data ingestion, feature engineering, modeling, validation, deployment, monitoring, and retraining. • Apply expertise across several core areas of machine learning and statistics (e.g., gradient-boosted models, deep neural networks, time series, causal inference concepts, experimentation design), selecting appropriate methods for complex data science problems. • Write efficient, modular, well-tested code for data processing, feature engineering, and model training/inference, leveraging distributed tooling (e.g., Vertex AI pipelines, Dataflow, BigQuery) where appropriate. • Design and implement robust validation frameworks for complex experiments and models, accounting for potential biases and real-world performance. • Troubleshoot complex model performance issues, data anomalies, and code bugs effectively with little guidance. • Define analytical approaches and scope data science projects for moderately complex or ambiguous business problems. • Partner with product managers and stakeholders to define success metrics and experiment goals, and to translate marketplace problems into data science solutions. • Lead the design and analysis of experiments (e.g., A/B tests, switchback) for your projects, and interpret complex model results and experimental outcomes with a focus on actionable insights and business outcomes. • Proactively identify opportunities within your domain where data science can provide significant value, and initiate exploration. • Follow and help improve established team processes for coding standards, documentation, reproducibility, and experimentation. • Mentor DS I and DS II scientists, providing technical guidance, reviewing code, analyses, and models, and supporting their growth in analytical and modeling skills. • Influence technical decisions within the team regarding modeling choices, validation strategies, and tooling through well-reasoned arguments and expertise. • Drive improvements to team standards, data science best practices, and analytical rigor; take ownership of specific team practices or technical components (e.g., a feature store component, leading experimentation reviews). • Educate stakeholders on the capabilities and limitations of data science models, and clearly explain complex methodologies and findings to both technical and non-technical audiences. • Participate actively in recruiting, providing high-quality, graded interview feedback for candidates up to this level.