Senior Machine Learning Scientist

Accountabilities:

• Lead the end-to-end development of machine learning solutions, from problem framing and data exploration through modeling, deployment, monitoring, and optimization.


• Design and implement recommendation systems, ranking models, predictive analytics, retrieval systems, classification models, and AI-powered user experiences.


• Develop and enhance LLM-based applications, including conversational agents, recommendation engines, content generation tools, and intelligent automation systems.


• Build robust evaluation frameworks, monitoring systems, and experimentation processes to ensure model quality, reliability, and business impact.


• Define and track both offline and online performance metrics, connecting model performance to key business outcomes such as retention, engagement, bookings, and revenue.


• Collaborate with engineering teams to deploy scalable production-grade machine learning systems while maintaining performance and latency standards.


• Partner with product, growth, and analytics stakeholders to identify opportunities, evaluate results, and influence strategic roadmap decisions.


• Establish best practices for experimentation, statistical rigor, model evaluation, and machine learning development across the organization.


• Research emerging machine learning techniques and apply practical innovations that improve product performance and user experience.

Requirements

• 5+ years of experience designing, building, and deploying machine learning models in production environments.


• Strong proficiency in Python, SQL, and modern software development practices for machine learning applications.


• Proven experience delivering end-to-end machine learning projects that generated measurable business impact.


• Deep expertise in at least one key area such as recommendation systems, search and ranking, natural language processing, embeddings, predictive modeling, causal inference, or LLM-based systems.


• Hands-on experience with modern AI technologies including prompt engineering, retrieval-augmented generation (RAG), vector databases, evaluation frameworks, and agent-based architectures.


• Strong understanding of experimentation methodologies, statistical analysis, A/B testing, and model performance evaluation.


• Experience collaborating closely with product and engineering teams in fast-paced, product-driven environments.


• Ability to balance cutting-edge innovation with practical, scalable business solutions.


• Excellent communication and documentation skills, with the ability to clearly explain technical concepts to diverse audiences.


• Advanced degree in Machine Learning, Computer Science, Statistics, Mathematics, Data Science, or a related field preferred; PhD or research experience is highly valued.

• Competitive salary package complemented by early-stage equity participation.


• Fully remote working environment with flexibility across European time zones, including the United Kingdom.


• High levels of ownership and autonomy over technical direction and project execution.


• Opportunity to work on cutting-edge machine learning, recommendation systems, and generative AI technologies.


• Collaborative and AI-native engineering culture that embraces modern development tools and rapid experimentation.


• Direct influence on product strategy and business outcomes through data-driven innovation.


• Flexible, asynchronous work environment that supports work-life balance.


• Professional growth opportunities within a rapidly scaling technology company.


• Exposure to challenging, real-world marketplace and consumer behavior problems at scale.


• Inclusive, collaborative culture that values curiosity, initiative, diversity of thought, and continuous improvement.

This listing is from ats_lever. View original listing ↗