Senior Applied Scientist, Machine Learning

Syndesus, Inc.; San Francisco (CA); Hybrid; USD 180,000 - 240,000; Full time

Job summary

Syndesus, Inc. is seeking a Senior Applied Scientist, Machine Learning to join the Consumer ML team. This hands-on role focuses on building and deploying ML solutions for personalization, pricing optimization, fraud detection, and customer journey improvements at scale.

You will lead end-to-end model development, design experimentation frameworks, and leverage deep learning, recommender systems, and reinforcement learning. GenAI tools will accelerate development.

Qualifications

• 8+ years in Applied Machine Learning or AI.
• 3+ years in a technical leadership or mentorship role.

Responsibilities

• Drive ML strategy across pricing, personalization, and recommendation systems.
• Design, build, and deploy ML models using behavioral and subscription data.
• Lead A/B and multivariate testing to evaluate model performance and impact.
• Leverage GenAI tools to accelerate development and experimentation.
• Apply deep learning, recommender systems, and representation learning techniques.
• Collaborate with product, marketing, engineering, and sales to translate ML insights into business value.
• Stay current with emerging ML techniques and contribute to internal knowledge sharing.

Skills

Python

SQL

PyTorch

Scikit-learn

Deep learning

Recommender systems

XGBoost

GitHub Copilot

Tools

Claude

GitHub Copilot

Job description

About Our Client

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.

Role Overview

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.

Key Responsibilities

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 comprehensivebenefits