Causal Marketing Scientist with Bayesian MMM

The Harris Poll; United States; On-site; USD 128,000 - 160,000; Full time

Benefits offered by this job

PTO plan

401k program

Paid parental leave

Tuition reimbursement

Commuter benefits

Job summary

BERA.ai is seeking a Data Scientist to join our Data Science team, with a focus on causal inference and marketing measurement. This role sits at the intersection of statistical modeling and brand strategy, helping our customers understand what drives brand and marketing performance - not just what correlates with it.

The ideal candidate is deeply curious about why marketing works, is comfortable working in Bayesian frameworks, and wants to apply rigorous causal methods to real marketing and

Qualifications

• Applied experience in causal inference and marketing measurement (MMM, attribution, or econometrics).
• Degree in statistics, data science, econometrics, or related quantitative field.
• Hands-on Bayesian modeling with priors, posteriors, and evaluating model fit.
• Ability to scope and execute causal modeling projects and communicate limitations.
• Strong written and verbal communication for non-technical stakeholders.
• Passion for brands and consumer behavior.

Responsibilities

• Design, build, and validate causal models for marketing and brand impact including MMM.
• Develop Bayesian models to quantify uncertainty and produce credible estimates.
• Test model assumptions, perform sensitivity analyses, iterate with new data.
• Translate statistical findings into actionable business insights for stakeholders.
• Collaborate with Product and Engineering to operationalize modeling outputs.

Skills

Causal inference

Marketing measurement

Bayesian modeling

Python (statistics)

Communication skills

Problem solving

Education

Bachelor's / Master's / PhD in Statistics or related

Tools

PyMC

Stan

pandas

numpy

scikit-learn

Job description

BERA.ai is seeking a Data Scientist to join our Data Science team, with a focus on causal inference and marketing measurement. This role sits at the intersection of statistical modeling and brand strategy, helping our customers understand what drives brand and marketing performance - not just what correlates with it.

The ideal candidate is deeply curious about why marketing works, is comfortable working in Bayesian frameworks, and wants to apply rigorous causal methods to real marketing and