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 Do Design and build Bayesian Marketing Mix Models (MMM) from the ground up using Py MC 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 (Di D), Synthetic Control models, and regression discontinuity analyses.

Develop advanced adstock and saturation transformations including geometric, Weibull, and Hill functions.

Perform

Bayesian model diagnostics using Arvi Z 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 Qualifications Master's or Ph D in Statistics, Mathematics, Economics, Computer Science, Data Science, Operations Research, or a related quantitative discipline.5+ years of experience building advanced statistical or econometric models in production environments. Strong expertise in Bayesian statistics and probabilistic modeling. Hands-on experience developing Bayesian models from scratch using Py MC and/or Stan.

Deep understanding of hierarchical Bayesian modeling and partial pooling techniques.

Experience implementing multi-stage probabilistic models with uncertainty propagation. Solid background in causal inference methodologies including Difference-in-Differences, Synthetic Control, geo experiments, and regression discontinuity. Expertise in nonlinear optimization using Sci Py Optimize,



CVXPY, or similar optimization frameworks.

Strong

Python programming skills with production-quality, modular, and reproducible code. Advanced SQL skills including joins, window functions, CTEs, and large-scale aggregations. Strong understanding of applied statistics including regression, hypothesis testing, probability distributions, and model evaluation.

Experience with Git-based version control and collaborative software development. Excellent written communication skills with experience documenting methodologies and technical assumptions. Ability to work independently with minimal supervision in a fast-paced consulting environment.

Preferred Qualifications Experience with Bayesian causal inference frameworks such as Causal Impact. Exposure to MLflow or similar experiment tracking platforms.

Experience with Num Pyro or Pyro.

Experience designing or analyzing geo lift experiments in marketing or media analytics. Knowledge of modern media measurement frameworks and marketing effectiveness analysis.

Experience working in cloud-based analytics environments (Azure, AWS, or GCP).

Technical Skills Pythonpandas Num

Pyscikit-learn SQLPy MCStan Arvi ZBayesian Statistics Hierarchical Bayesian Modeling Marketing Mix Modeling (MMM)Bayesian Regression Causal Inference Difference-in-Differences (Di D)Synthetic Control Regression Discontinuity Geo Lift Experiments Adstock Modeling Saturation Curves Hill Functions Weibull Distribution Change Point Detection Monte Carlo Simulation Sci Py Optimize CVXPYMulti-objective Optimization Experiment Design Git MLflow (Preferred)Num Pyro (Preferred)Pyro (Preferred)

📌 Senior Data Scientist – Marketing Mix Optimization (mmo Enhancements) (Hyderabad)
🏢 Blend
📍 Hyderabad