Job Posting Title Data Scientist - BERA

The Harris Poll; Northern (KY); Hybrid; USD 112,000 - 160,000; Full time

Benefits offered by this job

Medical, dental and vision coverage

PTO plan

401k program

Tuition reimbursement

Paid parental leave

Pre-tax commuter benefits

Job summary

BERA.ai is seeking a Data Scientist focused on causal inference and marketing measurement to join its Data Science team. The role blends statistical modeling with brand strategy to understand what drives marketing performance, leveraging Bayesian methods and probabilistic programming.

Two experience levels are welcomed: early career with a thesis in causal inference or econometrics, or 2–5 years applying causal methods in marketing analytics.

Qualifications

• Applied experience in causal inference and/or marketing measurement (MMM, attribution, incrementality).
• Strong foundation in causal inference, probability, and experimental design.

Responsibilities

• Design, build, and validate models measuring causal impact of marketing on outcomes.
• Develop Bayesian models to quantify uncertainty and produce credible estimates.
• Test model assumptions, perform sensitivity analyses, iterate with new data.
• Translate modeling results into actionable business insights for marketing leaders.
• Collaborate with Product and Engineering to operationalize modeling outputs.

Skills

Causal inference

Marketing measurement

Bayesian modeling

Python for modeling

Data visualization

Education

Bachelor's/Master's/PhD in Statistics, Data Science, Econometrics, or related quantitative field

Tools

PyMC

Stan

Job description

About Us

Data Scientist

BERA.ai is seeking a Data Scientist to join our Data Science team, with a specific 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 brand data. We're looking for someone who is genuinely passionate about brands and marketing, with an intellectual curiosity about what drives consumer behavior - not just someone applying models to whatever dataset happens to be in front of them.

We welcome applicants at two levels of experience:

• Early career: Recent graduate (MS or PhD) with a research or thesis focused on causal inference, econometrics, or Bayesian statistics, eager to apply that training to marketing problems.


• Experienced: 2-5 years of hands-on experience applying causal inference methods - such as marketing mix modeling (MMM), media/channel attribution, or econometric modeling -within marketing, advertising, brand, or consumer analytics.

Key Responsibilities

• Causal & Marketing Measurement Modeling: Design, build, and validate models that measure the causal impact of marketing and brand activities on business outcomes - including marketing mix models (MMM), incrementality testing, and other causal inference approaches (e.g., difference-in-differences, synthetic control, instrumental variables, Bayesian structural time series).


• Bayesian Modeling: Develop and refine Bayesian models (e.g., in PyMC, Stan, or similar probabilistic programming frameworks) to quantify uncertainty, incorporate prior domain knowledge, and produce credible, decision-ready estimates of marketing effectiveness.


• Model Validation & Iteration: Rigorously test model assumptions, perform sensitivity analysis, and iterate on modeling approaches as new data and marketing channels emerge.


• Business Insights and Communication: Serve as the translator between statistical rigor and marketing strategy. Communicate assumptions, causal findings, and their business implications clearly to both technical and non-technical stakeholders, including marketing and brand leaders.


• Cross-Functional Collaboration: Partner with Product and Engineering teams to help translate data science solutions into scalable, agentic product features - working closely with those teams as they operationalize and \"agentify\" modeling outputs into automated, product-facing workflows.

Required Qualifications

• Domain-Relevant Experience: Applied experience (or strong academic research) in causal inference and/or marketing measurement - e.g., MMM, attribution modeling, incrementality testing, or econometrics applied to marketing/brand/advertising data. Experience in unrelated domains (healthcare, education, life sciences, etc.) without a marketing/causal inference component is not a fit for this role.


• Educational Foundation: Bachelor's, Master's, or PhD in quantitative fields such as Statistics, Data Science, Economics, Econometrics, Applied Mathematics, or a related quantitative discipline, with strong grounding in causal inference, probability theory, and experimental design.


• Bayesian Fluency: Genuine, hands-on experience with Bayesian modeling - not just familiarity with the term. Comfortable specifying priors, working with posterior distributions, and evaluating model fit and uncertainty.


• Problem-Solving and Ownership: Ability to independently scope and execute causal modeling projects that answer real marketing questions, and to communicate the limitations and assumptions of those models honestly.


• Communication Skills: Strong written and verbal communication skills, with a demonstrated ability to translate technical modeling results - assumptions, uncertainty, causal claims - into clear, actionable insights for non-technical stakeholders such as marketing and brand leaders. Comfortable telling a data-driven story, not just presenting output.


• Passion for Brands & Consumer Behavior: A genuine interest in brands, marketing, and advertising, paired with intellectual curiosity about what drives consumer decision-making. Candidates should be motivated by the marketing questions themselves, not just the modeling techniques.

Technical Skills

• Programming & Modeling Tools: Strong Python skills specifically for statistical/causal modeling - proficiency with PyMC, Stan, or a comparable probabilistic programming framework is required. General-purpose Python experience (pandas, numpy, scikit-learn, etc.) is also expected.


• Statistical & Causal Methods: Deep, applied knowledge of marketing mix modeling, attribution, and causal inference techniques (e.g., Bayesian structural time series, difference-in-differences, synthetic control, instrumental variables, uplift modeling).


• Data Visualization: Ability to build clear, decision-ready visualizations and reports that communicate model outputs (e.g., channel contribution, ROI curves, credible intervals) to marketing stakeholders.


• Nice to Have: Exposure to marketing/advertising data structures such as media spend, impressions, brand tracking surveys. Deep database engineering, complex query optimization, or Big Data pipeline experience (Spark/PySpark) is not a requirement for this role.

Compensation

In order to comply with equal pay and salary transparency laws in various locations, this role follows a market-based compensation framework. Actual compensation is influenced by a wide array of factors including, but not limited to, skill set, level of experience, and geographic location.

• The anticipated base salary ranges by geographic zone are:
  
  
  
  • Zone 1 (High-Cost Market): $144,000 - $160,000
  
  
  • Zone 2(Mid-Cost Market): $128,000 - $144,000
  
  
  • Zone 3(Lower-Cost Market): $112,000- $128,000
  
  
  
  

• In addition to medical, dental and vision coverage, we offer a generous PTO plan, 401k program, comprehensive family planning benefits (including paid parental leave), tuition reimbursement, and pre-tax commuter benefits.


• Benefits/perks may vary depending on the nature of your employment with Stagwell and the location where you work.