Senior Applied Scientist, Machine Learning

United States·San Franciscofull-timesenior

Data ScientistData

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Overview

About Our Client Our client is a global technology company focused on consumer-facing digital products at massive scale.

Technical Tools

Data ScientistData

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.

Responsibilities

~1 min read

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

Requirements

~1 min read

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