Data Analyst, Quantitative Platform
About this role
The firm researches and trades quantitative investment strategies, which are implemented systematically via thousands of securities, spanning the world's major liquid asset classes. Founded in 1997 by David Harding, Winton today manages assets for some of the world’s largest institutional investors. We employ ambitious professionals who want to work collaboratively at the leading edge of investment management. Winton leverages quantitative analysis and cutting-edge technology to identify and capitalize on opportunities across global financial markets. We foster a collaborative and intellectually stimulating environment, bringing together individuals with Mathematics, Physics and Computer Science backgrounds who are passionate about applying rigorous scientific methods to financial challenges. As a fundamentally data-driven business, our success is heavily linked to the acquisition, processing, and analysis of vast datasets. High-quality, well-managed data forms the critical foundation for our quantitative research, strategy development, and automated trading systems. As a Data Analyst within our Quantitative Platform team, you will own the quality, consistency, and discoverability of datasets as they move from onboarding into production. You will uphold our data standards and catalogue, so datasets are easy to find, trust, and use. Your work spans vendor-sourced financial data, time series across instruments and asset classes, and complex, multi-table products where correct mapping and definitions matter as much as raw data accuracy. Your responsibilities will include: Defining and executing rigorous acceptance criteria for new and evolving data products. From sample evaluation through to production, including coverage analysis, staleness and gap detection, and reconciliation against trusted references where available. Acting as a subject-matter expert on our data products, helping Strategy Managers with vendor formats, data anomalies, corporate actions semantics, identifiers, and documentation gaps; escalate and track issues with vendors and internal stakeholders until resolved. Building and maintaining an automated catalogue of datasets (descriptions, owners, refresh cadence, SLAs, source systems, schemas, known limitations). Keeping the catalogue aligned with reality when pipelines change so consumers rely on current metadata. Systematically probe new and existing datasets to ensure they meet our high data quality standards. Stress-test point-in-time, versioning and revision semantics; chase down corrections, duplicates, staleness, and discontinuities with source vendors. Contributing to data quality frameworks, onboarding checklists, and documentation (data dictionaries, lineage notes, known limitations) so quality expectations are repeatable and auditable. Partnering with Data Engineers on handoff contracts (schemas, SLA expectations, alerting thresholds), with Quant Researchers on analytic sanity checks, and with operations on repeatable triage when anomalies appear in production datasets. What we are looking for: 3+ years’ experience working with financial data vendors and their products. Strong grasp of cross-asset class time series data and requirements from a quantitative strategy perspective Strong data modelling skills, with the ability to design clear entity relationships and validate that models match real-world data Hands-on analytical experience using Python, and the ability to summarize findings clearly for both technical and non-technical audiences. Meticulous attention to detail and a bias toward evidence-based conclusions. Excellent communication and collaboration skills, and the ability to work in a team in a fast-moving, data-centric environment. What would be advantageous: Direct experience with reference and hierarchical data (security masters, classification trees, entity relationships) and cross-vendor alignment. Familiarity with market, fundamental, or alternative datasets used in systematic or quantitative investment workflows. Exposure to data quality tooling or statistical monitoring (distributions, drift, anomaly detection) applied to production or near-production feeds. Experience working with ETL/ELT pipelines using Python. Practical experience using LLMs to accelerate complex data investigations. Equal Opportunity Workplace We are proud to be an equal opportunity workplace. We do not discriminate based upon race, religion, color, national origin, sex, sexual orientation, gender identity/expression, age, status as a protected veteran, status as an individual with a disability, or any other applicable legally protected characteristics.
What this role is, and what else it is called
Employers in London advertise this kind of work as Data Analyst, Senior Data Engineer, Data Engineer and Data Scientist too, so it is worth searching more than one wording. It is a mid-level Data, AI & Machine Learning role in London, advertised as on-site.
About hiring at Winton
Winton has 11 other live technology roles on its careers page, across Software Engineering, Data, AI & Machine Learning and Cloud & IT Infrastructure. Elsewhere in its adverts Winton asks for AWS, Kubernetes, Docker, Kafka and C#. We have been observing Winton hiring technology staff since June 2026. See all Winton roles, salaries and stack.
How this role compares to the market
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Technologies mentioned
Detected on Winton's careers page. ProdReady Recruitment lists this vacancy as an aggregator and is not the employer; applications go to the employer's own site. More IT jobs in London.