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 Job Description 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 simulations. Build optimization frameworks for marketing budget allocation using constrained nonlinear optimization techniques. Develop multi-objective optimization solutions balancing ROI, business constraints, and marketing objectives. Design and evaluate causal inference frameworks using geo experiments, Difference-in-Differences, Synthetic Control, and other quasi-experimental techniques. Implement adstock transformations, saturation functions (Geometric, Weibull, Hill curves), and response curve estimation for media effectiveness. Perform model diagnostics, posterior analysis, convergence validation, and uncertainty quantification using ArviZ and Bayesian diagnostic tools. Collaborate with business stakeholders to translate analytical findings into actionable marketing recommendations. Develop production-quality Python code, reusable modeling frameworks, and reproducible analytical workflows. Document modeling methodologies, assumptions, validation processes, and technical findings. Mentor junior data scientists and contribute to technical best practices across the organization. Qualifications Required Skills Programming & Data Science Strong Python programming skills with emphasis on production-quality, reusable, and maintainable code. Extensive experience with: Pandas NumPy Scikit-learn SciPy Strong SQL skills including: Complex joins Window functions Large-scale aggregations Performance optimization Experience using Git and version control best practices. Bayesian Modeling Strong hands-on experience building Bayesian regression models from first principles. Experience specifying: Priors Likelihood functions Posterior distributions Practical experience with: PyMC Stan (preferred) Strong understanding of: MCMC sampling Hamiltonian Monte Carlo (HMC) NUTS sampler Posterior predictive checks Experience interpreting Bayesian diagnostics including: R-hat Effective Sample Size (ESS) Divergences Trace plots Experience using ArviZ for posterior visualization and diagnostics. Marketing Mix Modeling Hands-on experience developing Marketing Mix Models (MMM). Experience implementing: Adstock transformations 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 Job Description As a Senior Data Scientist Marketing Mix Modeling (MMM) , you will play a critical role in designing and delivering advanced Bayesian Marketing Mix Mode