The Role
The Senior Applied Scientist will develop machine learning solutions for personalization, pricing optimization, and fraud detection, leading model development and experimentation while collaborating with various teams.
Summary Generated by Built In
About Our Client
Role Overview
Key Responsibilities
Qualifications
Work Environment
Why Join
Our client is a global technology company focused on consumer-facing digital products at massive scale. They leverage advanced machine learning and AI to deliver highly personalized user experiences, optimize monetization strategies, and improve customer outcomes across millions of users worldwide. The organization operates at the intersection of data science, product innovation, and real-time decisioning systems.
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.
ML Strategy & Ownership
- Drive machine learning strategy across pricing, personalization, and recommendation systems
- Identify opportunities to maximize customer value through data-driven decisioning
Model Development
- Design, build, and deploy ML models using behavioral and subscription data
- Develop systems for personalization, churn prediction, and conversion optimization
Optimization & Experimentation
- Lead A/B and multivariate testing to evaluate model performance
- Optimize customer journeys, pricing strategies, and monetization levers
Generative AI Enablement
- Leverage tools such as GitHub Copilot, Claude, and similar assistants
- Integrate GenAI into workflows to accelerate model development and experimentation
Advanced ML Techniques
- Apply 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 Collaboration
- Partner with Product, Marketing, Engineering, and Sales teams
- Translate ML insights into measurable business impact
Research & Innovation
- Stay 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 Skills
- 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)
Foundations
- Strong grounding in statistics, probability, linear algebra, and optimization
Communication
- Ability to clearly explain complex ML concepts to cross-functional stakeholders
- Proven ability to align technical solutions with business objectives
Work Environment
- Hybrid role based in Frisco, 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 comprehensive benefits
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The Company
What We Do
Syndesus builds engineering teams in Canada for US-based VC-backed startups, offering Employer of Record (PEO) services for remote employment and assisting with cross-border hiring and immigration issues.
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