Senior Data Scientist – Intelligent Media Targeting

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 As a Senior Data Scientist – Intelligent Media Targeting , you will play a key role in designing and deploying advanced customer analytics and marketing science solutions that enable intelligent media planning and customer targeting. You will work on customer segmentation, Marketing Mix Modeling (MMM), causal measurement, marketing effectiveness, and AI-powered analytics to help global clients optimize media investments and drive measurable business outcomes. This role requires strong expertise in machine learning, statistical modeling, customer analytics, and marketing measurement, along with the ability to translate complex analytical findings into actionable business recommendations. You will collaborate closely with business stakeholders, data engineers, and cross-functional teams to deliver scalable, production-ready data science solutions. Key Responsibilities Design and develop advanced customer segmentation models using clustering techniques such as K-Means, GMM, DBSCAN, or similar algorithms. Build and deploy Marketing Mix Models (MMM) to measure marketing effectiveness and optimize media investments. Engineer customer and transaction-level features including RFM, spend trajectory, recency decay, and behavioral metrics. Develop statistical and machine learning models for customer targeting, campaign optimization, and