Senior Data Scientist- London

• Significant professional experience in data science or advanced analytics.
• Strong hands-on experience developing statistical or machine-learning models.
• Strong analytical and problem-solving capability.
• The ability to explain how and why a technical approach has been selected, rather than simply producing an answer.
• Experience translating business questions into structured analytical problems.
• Strong communication and stakeholder-management skills.
• Confidence working directly with senior or non-technical business stakeholders.
• The ability to combine technical knowledge with commercial judgement.
• A genuine interest in financial markets, investment products or institutional investing.
• A transparent working style, including the confidence to acknowledge uncertainty and seek clarification where appropriate.

Preferred Domain Experience:

Candidates should ideally have worked in one or more of the following environments:

• Asset management
• Investment management
• Institutional investing
• Capital markets
• Wealth management
• Banking or broader financial services
• Financial markets or investment-product analytics

Direct knowledge of institutional clients and investment products, including areas such as ETFs, would be particularly valuable. Candidates coming exclusively from unrelated sectors may require considerably more time to develop the necessary business context.

Technical Skills:

The meeting established the need for a technically strong senior data scientist, although it did not prescribe a definitive technology stack. The following should therefore be treated as recommended screening criteria rather than confirmed client requirements:

• Python for data analysis and modelling
• SQL and experience working with complex datasets
• Statistical modelling and hypothesis testing
• Supervised and unsupervised machine learning
• Model evaluation, validation and interpretation
• Data visualisation and communication of analytical findings
• Feature engineering and data preparation
• Experience taking analytical work beyond initial exploration or proof of concept
• Understanding of how data models and semantic layers can support future AI-enabled analytics
• Exposure to cloud data platforms or modern machine-learning environments

The successful candidate is likely to be:

• Commercially aware and interested in how financial markets operate.
• Intellectually curious and motivated by difficult problems.
• Comfortable challenging assumptions and asking relevant questions.
• Able to communicate clearly and confidently.
• Logical and structured when working through unfamiliar scenarios.
• Honest when they do not know an answer, with the ability to explain how they would find one.
• Engaged with the wider purpose of the work, rather than focused only on building models.
• Capable of working independently without becoming disconnected from the business.

The hiring manager is particularly interested in candidates who demonstrate authentic reasoning. Perfect answers are less important than showing a logical thought process, recognising uncertainty and responding transparently when additional information is needed.