Manager Data Scientist - MMM at Blend360

We are seeking a highly skilled and experienced Manager Data Scientist with expertise in Media Mix Modeling (MMM) to lead advanced analytics initiatives and drive data-driven marketing effectiveness strategies. The ideal candidate will combine strong technical expertise with leadership capabilities to manage projects, mentor teams, and partner with business stakeholders to deliver actionable insights that optimize marketing investments across channels. Key Responsibilities: • Lead the development and implementation of Media Mix Models to optimize marketing spend across various channels (e.g., TV, digital, radio, print, social, etc.). • Manage end-to-end analytics projects including problem definition, data acquisition, modeling, validation, and business recommendations. • Partner with marketing, finance, and business leadership teams to align analytical solutions with strategic business objectives. • Build and oversee predictive and forecasting models to evaluate future marketing scenarios and guide budget allocation decisions. • Mentor and guide junior data scientists and analysts by providing technical leadership, code reviews, and best practices in analytics and modeling. • Present analytical findings, recommendations, and business impact clearly to senior stakeholders and executive leadership. • Drive deep-dive analyses on campaign performance, customer behavior, and media effectiveness to identify optimization opportunities. • Ensure accuracy, consistency, and integrity of data, models, and analytical outputs across projects. • Collaborate with cross-functional teams including Data Engineering, BI, Marketing, Product, and external partners to ensure seamless execution of analytics initiatives. • Establish scalable MMM frameworks, methodologies, and governance standards across the organization. • Stay updated on emerging trends in marketing analytics, causal inference, econometrics, machine learning, and MMM methodologies. • Create and maintain comprehensive documentation for models, processes, methodologies, and business assumptions.