Senior Data Scientist

About the role

We are seeking a highly skilled Senior Data Scientist to lead the design and delivery of AI-driven automation solutions that transform how Funds data is processed, validated, and distributed across the organisation. This role goes beyond experimentation - you will be responsible for building scalable, production-grade solutions that materially improve operational efficiency, data quality, and time-to-market for critical data products. You will play a key role in defining the AI strategy within Funds data workflows , leveraging Microsoft-based cloud technologies and modern machine learning approaches to solve complex, real-world challenges. As a senior individual contributor, you will combine deep technical expertise with strong business understanding , influencing stakeholders and guiding solutions from ideation through to full deployment and continuous optimisation.

Responsibilities

• Solution Design & Delivery
• Lead the end-to-end development of AI/ML solutions for Funds data workflows - from problem framing to production deployment and optimisation.
• Design scalable, maintainable architectures for data science and automation solutions within a Microsoft Azure ecosystem.
• Develop and implement machine learning, NLP, and AI-driven automation models to enhance data extraction, validation, and enrichment processes.
• Production & Engineering Collaboration
• Partner closely with engineering teams to industrialise models , ensuring robustness, monitoring, and performance in production environments.
• Contribute to best practices for MLOps , CI/CD, model lifecycle management, and performance tracking.
• Ensure solutions meet enterprise standards for security, scalability, and reliability .
• Business Impact & Stakeholder Engagement
• Translate complex business challenges into data science solutions with measurable impact (capacity gains, accuracy improvements, turnaround time reduction).
• Work directly with product, content, and operations teams to prioritise use cases and define success metrics .
• Act as a trusted advisor, influencing stakeholders on AI capabilities, limitations, and opportunities.
• Technical Leadership
• Provide technical guidance and mentorship to junior team members and peers.
• Contribute to the evolution of AI capabilities and standards within the organisation.
• Drive adoption of modern tools, frameworks, and approaches across the team.
• Data & Domain Expertise
• Build deep understanding of Funds data structures, workflows, and quality frameworks .
• Identify opportunities to standardise and automate data processing at scale .
• Communication & Documentation
• Clearly communicate findings, models, and recommendations to both technical and non-technical audiences .
• Produce well-documented, maintainable code and solution designs.

Requirements

• Technical Expertise
• Strong experience in Python-based data science and machine learning development in production environments.
• Proven experience delivering AI/ML solutions at scale , not just proofs of concept.
• Hands-on experience with Microsoft cloud technologies , including:
• Azure Machine Learning
• Azure AI Services
• Microsoft Fabric / OneLake
• Strong understanding of:
• Model evaluation, optimisation, and monitoring
• Data engineering concepts and pipelines
• MLOps practices and deployment frameworks
• Experience applying NLP and/or document processing techniques in real-world use cases is highly desirable.
• Experience & Impact
• Demonstrated ability to own and deliver complex data science projects end-to-end .
• Experience working with large-scale, structured and unstructured datasets .
• Track record of delivering measurable business outcomes through data science solutions.
• Leadership & Collaboration
• Ability to influence stakeholders and drive decision-making .
• Experience working in cross-functional teams across engineering, product, and operations .
• Mentorship or informal leadership experience is a strong advantage.
• Personal Qualities
• Strong problem-solving mindset with focus on practical, business-driven outcomes .
• High level of ownership and accountability.
• Curiosity and drive to stay current with emerging AI technologies (including LLMs and automation frameworks) .
• Ability to balance innovation with pragmatism and delivery .
• Degree (or equivalent experience) in Data Science, Computer Science, Mathematics, Engineering, or a related field.
• Several years of relevant industry experience in data science, machine learning, or AI engineering roles .
• Experience in financial services, market data, or similar domains is a strong advantage.
• Career Stage:
• Senior Associate
• Compensation Information:

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