Data Scientist - Bera

## About Us 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. ## 🔑 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. ## 🏅 Required Qualifications - Domain-Relevant Experience: Applied experience (or strong academic research) in causal inference and/or marketing measurement. - 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. - Bayesian Fluency: Genuine, hands-on experience with Bayesian modeling. - Problem-Solving and Ownership: Ability to independently scope and execute causal modeling projects that answer real marketing questions. - Communication Skills: Strong written and verbal communication skills to translate technical modeling results into actionable insights. - Passion for Brands & Consumer Behavior: A genuine interest in brands, marketing, and advertising, paired with intellectual curiosity. ## 🛠️ Technical Skills - Programming & Modeling Tools: Strong Python skills specifically for statistical/causal modeling — proficiency with PyMC, Stan, or a comparable probabilistic programming framework. - Statistical & Causal Methods: Deep, applied knowledge of marketing mix modeling, attribution, and causal inference techniques. - Data Visualization: Ability to build clear, decision-ready visualizations and reports.