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AI/ML Engineering
Data Scientist
Senior
London, United Kingdom
Hybrid
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Our client is a leading global investment management company headquartered in London. It manages over $228 billion in assets and serves institutional investors, pension funds, wealth managers, and other sophisticated clients worldwide. The firm specializes in quantitative investing, alternative investments, systematic trading strategies, and technology-driven asset management. Data science, machine learning, and AI are core components of its investment and research processes.
As part of our collaboration we will focus on two foundational capabilities required to enable safe and scalable AI adoption across the enterprise: Agentic Security and AI-Ready Data Foundations.
What project we have for you
We build the data foundations and evaluation frameworks that make AI useful, reliable and safe inside regulated financial firms. The value of an AI agent depends not only on the models behind it, but also on the quality of the structured and unstructured data it consumes and the accuracy, relevance and traceability of the outputs it produces. Your job is to measure that quality, identify where it breaks down and turn the findings into practical improvements.
This is a hands-on Data Scientist role for someone with strong statistical, analytical and data-engineering skills who is genuinely comfortable working with AI agents. You will evaluate underlying datasets, design benchmarks and quality metrics, analyse agent behaviour, investigate failure patterns and build automated evaluation and monitoring capabilities. You will work across documents, time-series data, research content and other complex data sources to ensure that agentic solutions are grounded in reliable evidence and produce outputs that can be trusted by investment professionals.
What you will do
- Evaluate the quality, completeness, consistency, relevance, and usability of structured and unstructured data used by AI agents, analytical models, and investment workflows.
- Develop frameworks and metrics for assessing agentic outputs, including factual accuracy, relevance, completeness, consistency, traceability, hallucination risk, and alignment with intended business outcomes.
- Design and maintain evaluation datasets, benchmarks, test scenarios, and validation processes for AI agents and data-driven solutions.
- Analyse documents, research materials, market data, time-series data, communications, and other unstructured sources to identify data quality issues, biases, gaps, and potential risks.
- Perform data wrangling, exploratory data analysis, statistical analysis, and visualisation to uncover meaningful patterns and support investment-related decision-making.
- Build automated data quality checks and monitoring capabilities for source data, data pipelines, model inputs, and agent-generated outputs.
- Collaborate with engineers, investment professionals, product stakeholders, and subject-matter experts to define evaluation criteria and translate business expectations into measurable quality standards.
- Develop Python-based analytical tools, prototypes, and evaluation pipelines using technologies such as Pandas, NumPy, Spark, and relevant AI-assisted development tools.
- Support the design and improvement of ETL and data preparation processes across structured and unstructured data sources.
- Create clear dashboards, reports, and visualisations that communicate data quality findings, model performance, agent behaviour, risks, and recommendations to both technical and non-technical stakeholders.
- Maintain reproducible analytical workflows through appropriate use of Git, documentation, testing, and development standards.
- Proactively identify opportunities to improve data foundations, agent performance, analytical processes, and investment decision support through data-driven solutions.
- Stay current with emerging approaches in agent evaluation, LLM observability, unstructured data processing, statistical validation, and agentic engineering.
What you need for this
- Proven experience in data wrangling, time-series analysis, statistical techniques and data visualisation especially across structured and unstructured data.
- Proficient in the Python data science stack (Pandas, NumPy, Spark, Matplotlib) with experience leveraging AI-assisted coding tools to accelerate development.
- Working knowledge of Snowflake, Linux/UNIX, Git, Jira.
- Passion for embracing agentic engineering — willingness and ability to work effectively with AI development tools as part of daily workflow.
- Ability to present technical results and concepts to non-technical audiences.
- Entrepreneurial mindset with a willingness to deeply understand investment strategies and proactively push data-driven solutions.
- Strong academic record with a degree in a STEM field.
Nice to have
- Previous experience working with investment professionals in a fast-paced environment preferred.
- Experience writing ETL pipelines and fluency in SQL preferred.
What it’s like to work at Intellias
At Intellias, where technology takes center stage, people always come before processes. By creating a comfortable atmosphere in our team, we empower individuals to unlock their true potential and achieve extraordinary results. That’s why we offer a range of benefits that support your well-being and charge your professional growth.
We are committed to fostering equity, diversity, and inclusion as an equal opportunity employer. All applicants will be considered for employment without discrimination based on race, color, religion, age, gender, nationality, disability, sexual orientation, gender identity or expression, veteran status, or any other characteristic protected by applicable law.
We welcome and celebrate the uniqueness of every individual. Join Intellias for a career where your perspectives and contributions are vital to our shared success.
