Note: The job is a remote job and is open to candidates in USA. BERA.ai is seeking a Data Scientist to join its Data Science team. The role focuses on causal inference and marketing measurement, developing Bayesian models, validating causal analyses, communicating insights to marketing stakeholders, and collaborating with Product and Engineering teams to operationalize data science solutions.
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
Skills
- * **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
- * **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
- * **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
Benefits
- Medical coverage
- Dental coverage
- Vision coverage
- Generous PTO plan
- 401k program
- Comprehensive family planning benefits, including paid parental leave
- Tuition reimbursement
- Pre-tax commuter benefits
Company Overview