Guide model deployment strategies including AIOps/MLOps, monitoring, model governance, and lifecycle management Develop reusable frameworks, accelerators, and best practices that improve the scalability and maintainability of AI solutions Mentor junior data scientists and provide thought leadership on emerging technologies such as agentic AI, knowledge graphs, and autonomous decision-making systems Requirements Bachelor''s degree in Data Science, Marketing Analytics, Applied Statistics, Economics, Computer Science, or a related field 7+ years of professional experience, with 5+ of those years in data science, advanced analytics, and machine learning applied to commercial, marketing, or consumer-facing problems 5+ years of experience designing, developing, validating and deploying machine learning solutions (e.g., TensorFlow, scikit-learn) in production environments, ideally supporting use cases such as marketing mix modeling, customer segmentation, personalization, pricing/promotion, demand or media optimization, or churn/lifetime-value prediction 3+ years of experience in Python/SQL for advanced analytics, data engineering, and model development, including work with marketing, sales, e-commerce, loyalty, or syndicated consumer datasets (e.g., Nielsen/IRI, CRM, digital media, or POS data) 1+ years of experience designing, fine-tuning, evaluating, and deploying LLM-based applications for commercial or marketing use cases (e.g., content generation, campaign optimization, or consumer insight mining) 1+ years of experience with cloud computing platforms (e.g., Azure, AWS, GCP) and production AI system design Other Master''s or PhD in Data Science, Applied Statistics, Marketing Analytics, Economics, Computer Science, or a related field (or an MBA) is preferred Experience partnering with Marketing, Sales, and Consumer Insights teams to design AI-enabled workflows across areas such as campaign optimization, consumer engagement, pricing and promotion analytics, media/marketing mix modeling, or digital commerce is preferred Familiarity with the commercial/CPG go-to-market landscape - including retail media, shopper marketing, category management, or direct-to-consumer channels is preferred Knowledge of database systems, data warehousing, and distributed computing frameworks (e.g., SQL, NoSQL, Hadoop, Spark, or Ray) is preferred Understanding of agentic AI, knowledge graphs, and autonomous decision-making systems, along with expertise in GenAI models (e.g., GANs and diffusion models) and building LLM-based solutions such as RAG pipelines, copilots, and multimodal AI applications, is preferred Experience operationalizing AI solutions through AIOps/MLOps frameworks, model governance, and enterprise AI lifecycle management, including responsible AI principles such as fairness, explainability, hallucination detection, and AI risk management, is preferred Don't meet all the qualifications listed under "other"? Lead the design, development, validation, and deployment of advanced statistical, machine learning, optimization, and AI solutions to solve complex commercial and marketing business problems Design, develop, evaluate, and deploy statistical, predictive, optimization, machine learning, and Generative AI models for consumer-facing and go-to-market applications Design and operationalize GenAI solutions, including RAG pipelines, AI agents, copilots, and multimodal AI applications, where appropriate to power marketing and commercial workflow processes Partner with brand, marketing, sales, and category leaders to frame business problems, identify analytical opportunities, translate strategic priorities into AI and advanced analytics solutions, and drive adoption across retail and consumer channels.