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 Job Description 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.

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 structures and multi-stage/chained architectures with proper uncertainty propagation. Build adstock and saturation transformations to model channel-level response curves and extract actionable insights from posterior distributions. Design and analyze causal attribution studies (geo experiments, DiD, Synthetic Control) to calibrate and validate model outputs against real-world lift. Build constrained and multi-objective optimization models (scipy, CVXPY) to recommend budget allocations across channels, respecting business constraints and floors/ceilings. Disaggregate coarse budget plans into monthly/channel-level media plans using temporal disaggregation techniques. Integrate Gen AI/LLM tools into analytics workflows to automate narrative generation, insight summarization, and reporting. Partner with marketing, media, and business stakeholders to translate analytical outputs into clear recommendations and decision-support tools. Document methodology, assumptions, and model limitations in structured write-ups to ensure reproducibility and transparency across the team. Work independently against a defined brief, proactively flagging risks, data gaps, or blockers to stakeholders. Segmentation & Media Targeting Customer segmentation: K-Means, GMM, DBSCAN — understands the underlying mathematics, not just the API Cluster validation: silhouette score, stability testing, business interpretability Feature engineering on transaction data: multi-window RFM, spend trajectory, recency decay, time spine construction SHAP explainability: interpreting feature importance and translating it into plain-English segment narratives Competitive metrics: Spend Index (issuer) and Wallet Share (merchant) — calculation and correct interpretation, including network coverage limitations Temporal disaggregation: Denton-Cholette or equivalent — distributing coarse