Senior Applied Scientist, Machine Learning

Syndesus•Frisco, TX

8h•Hybrid

About The Position

Our client is seeking a Senior Applied Scientist, Machine Learning to 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.

Requirements

8+ years in Applied Machine Learning or AI
3+ years in a technical leadership or mentorship capacity
Personalization and recommendation systems
Dynamic pricing or offer optimization
Churn / propensity modeling for subscription products
Strong 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)
Strong grounding in statistics, probability, linear algebra, and optimization
Ability to clearly explain complex ML concepts to cross-functional stakeholders
Proven ability to align technical solutions with business objectives

Nice To Haves

Implement reinforcement learning approaches such as contextual bandits, Q-learning, or Thompson sampling

Responsibilities

Drive machine learning strategy across pricing, personalization, and recommendation systems
Identify opportunities to maximize customer value through data-driven decisioning
Design, build, and deploy ML models using behavioral and subscription data
Develop systems for personalization, churn prediction, and conversion optimization
Lead A/B and multivariate testing to evaluate model performance
Optimize customer journeys, pricing strategies, and monetization levers
Leverage tools such as GitHub Copilot, Claude, and similar assistants
Integrate GenAI into workflows to accelerate model development and experimentation
Apply deep learning, recommender systems, and representation learning
Implement reinforcement learning approaches such as contextual bandits, Q-learning, or Thompson sampling
Partner with Product, Marketing, Engineering, and Sales teams
Translate ML insights into measurable business impact
Stay current with emerging ML techniques and industry trends
Contribute to internal knowledge sharing and external thought leadership