Data Scientist

BayOne Solutions; United States; On-site; USD 96,432 - 103,320; Full time

Job summary

BayOne Solutions is seeking a hands-on Senior Data Scientist to build next-generation marketing measurement solutions, focusing on Bayesian Media Mix Modeling (MMM). You will develop models from first principles in Python and collaborate with analytics partners to drive data-driven decisions.

The role involves building MMMs, integrating experiments and geo tests, and delivering probabilistic forecasts and scalable modeling workflows for marketing investments.

Qualifications

• Master's or PhD in a quantitative field.
• 5+ years of statistical or ML modeling experience.
• Strong Python and Bayesian/MCMC modeling experience.
• Experience in MMM, marketing measurement, causal inference, econometrics.
• Ability to build models from first principles rather than off-the-shelf.
• Familiarity with AI-assisted development tools (e.g., Copilot, ChatGPT).

Responsibilities

• Develop and enhance Bayesian models for marketing measurement.
• Build and improve MMM and integrate experiments and geo tests.
• Develop probabilistic forecasting and scenario simulations.
• Create reusable Python libraries and automate modeling workflows.
• Communicate findings to cross-functional partners.

Skills

Bayesian statistics

MCMC modeling

Time series forecasting

Causal inference

Marketing analytics

Education

Master's or PhD in a quantitative field

Tools

Python

GitHub Copilot

BigQuery

Spark

Databricks

Job description

Duration: Long term contract (12+ Months Contract)

Pay rate: $70/HR - $75/HR

About the Role

We are seeking a hands-on Senior Data Scientist to help build next-generation marketing measurement solutions, with a focus on Bayesian Media Mix Modeling (MMM). This role is ideal for someone who enjoys developing statistical models from first principles and applying them to solve challenging business problems, rather than simply configuring off-the-shelf modeling packages.

Responsibilities

• Develop and enhance Bayesian statistical models for marketing measurement using Python, including hierarchical models, feature engineering, prior specification, MCMC sampling, response curves, and model validation.
• Build and improve Media Mix Models (MMM) and integrate insights from experimentation, geo tests, and other causal measurement approaches.
• Develop probabilistic forecasting and scenario simulations to support marketing investment decisions.
• Build reusable Python libraries, automate modeling workflows, and partner with engineering teams to improve scalability and reproducibility.
• Communicate technical findings and recommendations to cross-functional business and analytics partners.

Qualifications

• Master's or PhD in Statistics, Economics, Mathematics, Computer Science, Operations Research, or another quantitative discipline.
• 5+ years of experience developing statistical or machine learning models in either academic or industry.
• Strong Python programming skills and hands-on experience with Bayesian/MCMC modeling.
• Experience in one or more of the following areas:
• Media Mix Modeling (MMM)
• Marketing Measurement or Marketing Science
• Causal Inference and Experimentation
• Econometrics
• Time Series Forecasting or Demand Modeling
• Demonstrated ability to develop and customize statistical models from first principles rather than relying solely on existing modeling packages.
• Familiarity with AI-assisted development tools (e.g., GitHub Copilot, ChatGPT) to improve modeling and engineering productivity.

Preferred Qualifications

• Experience with retail, eCommerce, or digital marketing analytics.
• Experience with geo experiments, A/B testing, uplift modeling, or incrementality measurement.
• Experience with BigQuery, Spark, Databricks, or other cloud analytics platforms.