Senior Machine Learning Scientist
New
Fully remote working environment with flexibility across European time zones, including the United Kingdom., European time zonesFull-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 5+ years
- Required Skills
- PythonSQLMachine LearningA/B testingNLPLLM
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.
Responsibilities
- 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.
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