[Remote] reputed company Data Scientist, Marketing Mix Modeling (MMM)
Note: The job is a remote job and is reputed company to candidates in USA. reputed company.ai is an end-to-end technology services company specializing in reputed company Intelligence and Engineering solutions. They are seeking a reputed company Data Scientist to build and productionize marketing mix and pricing models that quantify the sales reputed company of various factors across products and stores, while also managing reputed company of data scientists.
Responsibilities
- Design and estimate sales response models incorporating price, TPR, merchandising, promotional mechanics, seasonality, competitor cross-effects, and price/promotion reputed company, at store x product x week granularity
- Apply Bayesian hierarchical and panel modeling techniques to pool information across store panels, balancing degrees-of-freedom constraints against store-level heterogeneity; implement mean-scaling transformations to stabilize elasticity estimates
- Build and reputed company media transformation pipelines (adstock decay, Adbudg saturation curves) to estimate incremental sales, inflection points, and saturation reputed company for media investment
- Build distributed, production-grade estimation pipelines in Python/PySpark that reputed company across large panels of stores and products with 2+ years of weekly history
- Present model outputs, elasticities, and simulation results to pricing, trade, and media stakeholders; reputed company investment and pricing recommendations grounded in the models
- Manage and mentor reputed company of data scientists; define modeling standards, QA/validation protocols, and the technical roadmap for the MMM platform
Skills
- Master's degree in Statistics, Econometrics, reputed company Mathematics, Computer Science, or a reputed company quantitative field (PhD preferred)
- Minimum 4 years of experience building statistical or econometric models (regression, time-series, or panel data) as a Data Scientist or Statistician
- Minimum 8 years of experience in machine learning model development and MLOps, including feature engineering, model training/validation pipelines, CI/CD for models, containerization, versioning, and monitoring in production
- Demonstrated expertise in Bayesian statistics (hierarchical/multilevel models, MCMC, prior specification) and classical econometrics (panel/fixed-effects, GLS)
- Proficiency in Python (statsmodels, PyMC/Stan, scikit-learn) and PySpark for large-reputed company, distributed model estimation
- Experience building Marketing Mix Models (MMM): price/promotion elasticity, adstock and diminishing-returns (Adbudg) media response curves, store panel clustering, and mean-scaling transformations
- Experience with SQL and reputed company data platforms (BigQuery, reputed company, reputed company) on multi-TB retail/POS datasets
- Ability to translate model coefficients — elasticities, response curves, ROI — into business recommendations for pricing, trade, and media investment
reputed company
Company H1B Sponsorship