Blend is seeking Senior Data Scientists to join our Marketing Mix Optimization (MMO) Enhancements team, building next-generation Bayesian Marketing Mix Models that help global clients optimize media investments and maximize business outcomes. This role is ideal for professionals who have deep expertise in Bayesian statistics, causal inference, optimization, and advanced econometric modeling. As a Senior Data Scientist, you will develop production-grade Bayesian models, design optimization frameworks, evaluate marketing effectiveness, and translate complex statistical methodologies into actionable business recommendations. You will work closely with data scientists, engineers, and business stakeholders to build scalable, interpretable, and production-ready solutions. What You'll D oDesign and build Bayesian Marketing Mix Models (MMM) from the ground up using PyMC or Stan .Develop hierarchical Bayesian models capable of handling sparse and multi-level marketing data .Implement chained and multi-stage modeling architectures with proper uncertainty propagation using Monte Carlo simulations .Build constrained optimization models for media budget allocation considering channel constraints, business rules, and ROI objectives .Develop multi-objective optimization frameworks balancing multiple marketing and business KPIs .Design and analyze geo experiments, causal impact studies, Difference-in-Differences (DiD), Synthetic Control models, and regression discontinuity analyses .Develop advanced adstock and saturation transformations including geometric, Weibull, and Hill functions .Perform Bayesian model diagnostics using ArviZ including convergence analysis, posterior validation, R-hat, ESS, and divergence analysis .Build reproducible, production-quality Python code following software engineering best practices .Work extensively with large-scale datasets using SQL, Python, and statistical modeling techniques .Document methodologies, modeling assumptions, validation approaches, and technical decisions for business stakeholders .Collaborate with engineering teams to operationalize statistical models into production environments .Independently own workstreams while proactively identifying risks, blockers, and improvement opportunities . Required Qualificatio nsMaster's or PhD in Statistics, Mathematics, Economics, Computer Science, Data Science, Operations Research, or a related quantitative disciplin e.5 years of experience building advanced statistical or econometric models in production environment s.Strong expertise in Bayesian statistics and probabilistic modelin g.Hands-on experience developing Bayesian models from scratch using PyMC and/or Sta n.Deep understanding of hierarchical Bayesian modeling and partial pooling technique s.Experience implementing multi-stage probabilistic models with uncertainty propagatio n.Strong background in causal inference methodologies including Difference-in-Differences, Synthetic Control, geo experiments, and regression discontinuit y.Expertise in nonlinear optimization using SciPy Optimize, CVXPY, or similar optimization framework s.Strong Python programming skills with production-quality, modular, and reproducible cod e.Advanced SQL skills including joins, window functions, CTEs, and large-scale aggregation s.Strong understanding of applied statistics including regression, hypothesis testing, probability distributions, and model evaluatio n.Experience with Git-based version control and collaborative software developmen t.Excellent written communication skills with experience documenting methodologies and technical assumption s.Ability to work independently with minimal supervision in a fast-paced consulting environmen t. Preferred Qualificati onsExperience with Bayesian causal inference frameworks such as CausalImpa ct.Exposure to MLflow or similar experiment tracking platfor ms.Experience with NumPyro or Py ro.Experience designing or analyzing geo lift experiments in marketing or media analyti cs.Knowledge of modern media measurement frameworks and marketing effectiveness analys is.Experience working in cloud-based analytics environments (Azure, AWS, or GC P). Technical Sk illsPy thonpa ndasN umPyscikit-l ear nSQL PyMC StanA rviZBayesian Statis ticsHierarchical Bayesian Mode lingMarketing Mix Modeling ( MMM)Bayesian Regres sionCausal Infer enceDifference-in-Differences ( DiD)Synthetic Con trolRegression Discontin uityGeo Lift Experim entsAdstock Mode lingSaturation Cu rvesHill Funct ionsWeibull Distribu tionChange Point Detec tionMonte Carlo Simula tionSciPy Opti mizeC VXPYMulti-objective Optimiza tionExperiment De sig nGitMLflow (Prefer red)NumPyro (Prefer red)Pyro (Prefer red)