[Remote] Data Scientist - BERA

Note: The job is a remote job and is open to candidates in USA. The Harris Poll is seeking a Data Scientist to join the BERA.ai Data Science team. The role focuses on causal inference and marketing measurement, including developing Bayesian models, validating causal methods, translating findings into business insights, 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

• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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)
• Ability to build clear, decision-ready visualizations and reports that communicate model outputs (e.g., channel contribution, ROI curves, credible intervals) to marketing stakeholders
• 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; benefits/perks may vary depending on the nature of your employment with Stagwell and the location where you work

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