$ cat jobs/senior-data-scientist-marketing-mix-modeling-mmm-blend360-39493c3b3659.json
Senior Data Scientist – Marketing Mix Modeling (MMM)
Blend is a premier AI services provider, committed to co-creating meaningful impact for its clients through the power of data science, AI, technology, and people. With a mission to fuel bold visions, Blend tackles significant challenges by seamlessly aligning human expertise with artificial intelligence. The company is dedicated to unlocking value and fostering innovation for its clients by harnessing world-class people and data-driven strategy. We believe that the power of people and AI can have a meaningful impact on your world, creating more fulfilling work and projects for our people and clients. For more information, visit www.blend360.com As a Senior Data Scientist – Marketing Mix Modeling (MMM) , you will play a critical role in designing and delivering advanced Bayesian Marketing Mix Modeling solutions that help clients optimize marketing investments, improve media effectiveness, and drive measurable business outcomes. You will develop statistical models, causal inference frameworks, and optimization solutions that enable data-driven marketing decisions across global enterprises. This role requires deep expertise in Bayesian statistics, probabilistic programming, causal inference, optimization, and marketing analytics. You will collaborate closely with cross-functional teams, including data engineers, product owners, business stakeholders, and client leadership, to build scalable, production-ready analytics solutions. Key Responsibilities Design, develop, and deploy advanced Marketing Mix Models (MMM) using Bayesian statistical methodologies. Build Bayesian regression models with appropriate likelihoods, priors, and hierarchical model structures. Develop probabilistic models using PyMC and/or Stan for marketing effectiveness measurement. Design and implement hierarchical Bayesian models to improve estimation across sparse or multi-level datasets. Develop chained and multi-stage modeling frameworks with proper uncertainty propagation using Monte Carlo simulat
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