LeadMachineLearningScientist

## Accountabilities In this role, you will define and lead the machine learning strategy for marketing and growth, identifying high-value opportunities and building scalable systems that improve acquisition, retention, and monetization. You will partner closely with cross-functional teams to ensure ML initiatives align with business priorities and financial outcomes. Key responsibilities include: Leading the end-to-end ML strategy for marketing and growth, including roadmap definition, prioritization, and execution across teams. Designing and deploying production-grade models such as propensity, churn prediction, customer lifetime value, and next-best-action systems. Improving targeting, segmentation, onboarding, personalization, and engagement across multiple financial products. Driving experimentation frameworks (A/B testing, uplift modeling) to measure incrementality and optimize marketing efficiency. Identifying and integrating new internal and external data sources to enhance model performance and business insight. Building scalable, measurable ML systems that are tightly integrated into marketing and product workflows. Establishing technical standards for modeling, validation, experimentation, and deployment across the ML lifecycle. Optimizing reward, incentive, and promotion strategies to maximize ROI while controlling costs and improving conversion efficiency. Requirements This role requires deep expertise in machine learning and applied data science, combined with strong business acumen and the ability to lead complex, cross-functional initiatives. The ideal candidate is both strategic and hands-on, capable of owning models from design through production deployment. You should bring: 7+ years of experience in machine learning, data science, or applied AI roles with production-scale impact. Proven experience building and deploying marketing-related ML models such as propensity, churn, LTV, and recommendation systems. Strong programming skills in Python and S