Marketing Mix Modeling Data Scientist (MMM) - ONLY FULLTIME

Job Title – Lead Data Scientist, Marketing Mix Modeling (MMM)

About the Company

Blackstraw.ai is an end-to-end technology services company specializing in Artificial Intelligence (AI) and Engineering solutions across Data Science, Data Engineering, LLM/GenAI and LLMOps. Founded in 2018, we help global enterprises across North America, Europe and Asia to build and operationalize AI systems that create measurable business impact. Our mission is to make AI adoption simpler, faster and scalable through a blend of deep domain expertise, reusable accelerators and proven engineering practices.

With a 400+ strong team of engineers, data scientists and AI specialists, we partner with organizations to deliver real-world outcomes in areas such as predictive analytics, computer vision, natural language processing and Generative AI. Headquartered in Florida (USA) with operations in Canada and India, Blackstraw.ai continues to empower global enterprises to unlock the true potential of AI.

Experience: 8 to 15 yrs

Location: USA

Work Mode: Remote

Minimum qualifications

• Master’s degree in Statistics, Econometrics, Applied Mathematics, Computer Science, or a related 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-scale, distributed model estimation.

Preferred qualifications

• 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 cloud data platforms (BigQuery, Snowflake, Databricks) on multi-TB retail/POS datasets.

• Ability to translate model coefficients — elasticities, response curves, ROI — into business recommendations for pricing, trade, and media investment.

About the Job

Using Bayesian and classical statistical methods, you build and productionize marketing mix and pricing models that quantify the sales impact of price, promotion, media, and competitive activity across thousands of products and stores. You do more than fit models — you validate assumptions, defend model specification choices, and translate elasticities and response curves into investment recommendations for pricing, trade, and media teams.

With your leadership, you manage a team of data scientists, set modeling standards, and own the technical roadmap for the modeling platform end to end.

Responsibilities

• Design and estimate sales response models incorporating price, TPR, merchandising, promotional mechanics, seasonality, competitor cross-effects, and price/promotion thresholds, 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 calibrate media transformation pipelines (adstock decay, Adbudg saturation curves) to estimate incremental sales, inflection points, and saturation levels for media investment.

• Build distributed, production-grade estimation pipelines in Python/PySpark that scale 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; make investment and pricing recommendations grounded in the models.

• Manage and mentor a team of data scientists; define modeling standards, QA/validation protocols, and the technical roadmap for the MMM platform.

Key traits: 

• Be a problem solver and be proactive to solve the challenges that come his way.

• Should have excellent communication skills.

• Should be self motivated and willing to work as part of a team.

• Should be able to collaborate and coordinate in a remote environment.

• Should create a positive, and friendly environment for your team and colleagues from outside your team

• Should nurture innovation and learn-by-doing culture within the team
   

Blackstraw provides equal employment opportunities to applicants and employees without regard to race, color, religion, age, sex, sexual orientation, gender identity/expression, national origin, marital status, protected veteran status, disability status, or any other basis as protected by federal, state, or local law