Data Analyst

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.

• 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; escalating and tracking 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.

• 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.

• 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.
• Practical experience using LLMs to accelerate complex data investigations.

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.