Senior Applied Scientist, Machine Learning

Role Overview: Our client is seeking aSenior Applied Scientist, Machine Learningto join their Consumer ML team. This is a hands-on, high-impact role focused on building and deploying machine learning solutions that drive personalization, pricing optimization, fraud detection, and customer journey improvements. You will lead end-to-end model development, design experimentation frameworks, and leverage cutting-edge techniques including deep learning, recommender systems, and reinforcement learning. This role also emphasizes adoption of GenAI tools to accelerate development and innovation.

Key Responsibilities: ML Strategy & OwnershipDrive machine learning strategy across pricing, personalization, and recommendation systems Identify opportunities to maximize customer value through data-driven decisioning Model DevelopmentDesign, build, and deploy ML models using behavioral and subscription data Develop systems for personalization, churn prediction, and conversion optimization Optimization & ExperimentationLead A/B and multivariate testing to evaluate model performance Optimize customer journeys, pricing strategies, and monetization levers Generative AI EnablementLeverage tools such as GitHub Copilot, Claude, and similar assistants Integrate GenAI into workflows to accelerate model development and experimentation Advanced ML TechniquesApply deep learning, recommender systems, and representation learning (Nice to have) Implement reinforcement learning approaches such as contextual bandits, Q-learning, or Thompson sampling Cross-Functional CollaborationPartner with Product, Marketing, Engineering, and Sales teams Translate ML insights into measurable business impact Research & InnovationStay current with emerging ML techniques and industry trends Contribute to internal knowledge sharing and external thought leadership

Qualifications: Experience 8+ years in Applied Machine Learning or AI 3+ years in a technical leadership or mentorship capacity Domain Expertise (Must Have at least one) Personalization and recommendation systems Dynamic pricing or offer optimization Churn / propensity modeling for subscription products Technical SkillsStrong background in classical ML and deep learning (e.g., XGBoost, Random Forest, neural networks) Experience with recommender systems and representation learning Proficiency in Python, SQL, and ML frameworks (e.g., PyTorch, Scikit-learn) FoundationsStrong grounding in statistics, probability, linear algebra, and optimization CommunicationAbility to clearly explain complex ML concepts to cross-functional stakeholders Proven ability to align technical solutions with business objectives Work EnvironmentHybrid role based inFrisco, TX Candidates must be within commuting distance No relocation support available

Why Join: Work on high-scale, real-world ML problems impacting millions of users Strong investment in AI/ML innovation and tooling (including GenAI) Collaborative, cross-functional environment with clear business impact Competitive compensation, bonus structure, and comprehensivebenefits