Senior Data Scientist – Marketing Mix Optimization (MMO Enhancements)

Blend; Hyderabad; On-site; INR 2,500,000 - 4,000,000; Full time

Job summary

Blend is seeking a Senior Data Scientist to join the Marketing Mix Optimization (MMO) Enhancements team. You will build next-generation Bayesian Marketing Mix Models to help global clients optimize media investments and maximize business outcomes.

You will translate complex statistical methods into actionable business recommendations, work with data scientists, engineers, and stakeholders, and deliver scalable, production-ready solutions with transparent uncertainty quantification.

Qualifications

• Master's or PhD in a quantitative field.
• 5+ years of experience building advanced statistical or econometric models in production environment.
• Strong expertise in Bayesian statistics and probabilistic modeling.
• Hands-on experience developing Bayesian models from scratch using PyMC and/or Stan.
• Deep understanding of hierarchical Bayesian modeling and partial pooling.

Responsibilities

• Design and build Bayesian Marketing Mix Models (MMM) from the ground up.
• Develop hierarchical Bayesian models for sparse/multi-level data.
• Implement multi-stage modeling with uncertainty propagation using Monte Carlo simulations.
• Create constrained optimization models for media budget allocation.
• Develop multi-objective optimization balancing marketing KPIs.
• Design geo experiments and causal inference studies including DiD and Synthetic Control.
• Prepare production-ready Python code following software engineering practices.
• Collaborate with data engineers and business stakeholders to operationalize models.

Skills

Bayesian statistics

Probabilistic modeling

Causal inference

Python

SQL

Optimization

Data visualization

Git

ArviZ

Education

Master's or PhD in Statistics/Mathematics/Economics/CS/Data Science/OR

Tools

PyMC

Stan

ArviZ

NumPyro

Job description

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

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