Data Analyst in London

About Winton

Winton is a research-based investment management company with a specialist focus on statistical and mathematical inference in financial markets. 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, 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 probing new and existing datasets to ensure they meet our high data quality standards.
• Stress-testing point-in-time, versioning and revision semantics; chasing 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 what common or nuanced issues can arise when onboarding new datasets.
• Comfort with complex, multi-entity datasets (join keys, slow-changing dimensions, snapshots vs history) and a methodical approach to debugging inconsistencies.
• 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 building 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.