Senior / Lead Data Scientist – Media Targeting and Media Mix Optimization

Blend is a premier AI services provider, committed to co-creating meaningful impact for its clients through the power of data science, AI, technology, and people. With a mission to fuel bold visions, Blend tackles significant challenges by seamlessly aligning human expertise with artificial intelligence. The company is dedicated to unlocking value and fostering innovation for its clients by harnessing world-class people and data-driven strategy. We believe that the power of people and AI can have a meaningful impact on your world, creating more fulfilling work and projects for our people and clients. For more information, visit www.blend360.com We are looking for a Senior/Lead Data Scientist with strong expertise in Media Targeting and Media Mix Optimization to design, enhance, and optimize marketing investment strategies using advanced statistical modeling, machine learning, and optimization techniques. The ideal candidate will have experience building scalable optimization solutions that help maximize marketing ROI and improve budget allocation across channels. This role requires excellent Python programming skills, strong statistical foundations, and the ability to translate complex analytical findings into actionable business recommendations. Key Responsibilities: Design and build customer segmentation models (K-Means, GMM, DBSCAN) on large-scale transaction data to power media targeting strategy. Engineer features from raw transaction data — RFM variants, spend trajectories, recency decay — to support segmentation and downstream modeling. Validate clusters for statistical robustness and business interpretability, and translate segment-level patterns into clear, actionable narratives using SHAP and similar explainability techniques. Calculate and interpret competitive metrics (Spend Index, Wallet Share) to inform targeting and positioning decisions. Develop and maintain Bayesian marketing mix models (PyMC/Stan) from first principles, including hierarchical struc