Job Title: Senior Machine Learning Scientist

Location: Fully Remote (Europe)

Contract: 6 months

About the Role

We’re working with a forward-thinking client who are investing heavily in applied machine learning to power new products and intelligent features. They are looking for a Senior Machine Learning Scientist to join on a 6-month remote contract. You’ll be part of a team that thrives on experimentation, solving complex problems, and deploying models that deliver real value at scale.

This is a role for someone who is already operating as a Machine Learning Scientist and is ready to take ownership of challenging projects, from early research to production.

What You’ll Do

• Design and develop advanced ML models to tackle business-critical problems, ranging from content understanding and personalisation to classification and recommendation.
• Lead initiatives to fine-tune and scale models, ensuring they are robust and production-ready.
• Collaborate with cross-functional teams to turn open-ended ideas into clearly defined ML solutions.
• Run large-scale experiments and A/B tests, applying statistical rigor to evaluate models and guide product decisions.
• Contribute to the long-term ML strategy by staying close to cutting-edge research and sharing best practices across the team.
• Communicate complex findings to technical and non-technical stakeholders, shaping decisions at both team and leadership level.

What We’re Looking For

• Currently working as a Machine Learning Scientist, with significant hands-on experience delivering models into production.
• Strong expertise in Python and deep learning frameworks such as PyTorch or TensorFlow.
• Solid understanding of MLOps principles, data pipelines, and scalable ML practices.
• Experience with experiment design, A/B testing, and translating results into actionable insights.
• Familiarity with Databricks, PySpark, or similar platforms for large-scale data processing.
• Strong communication skills and the ability to influence technical direction in a collaborative environment.
• A track record of independently driving ML projects in ambiguous or fast-moving settings.