Data Analyst

Winton Capital US LLC; Greater London; Hybrid; GBP 60,000 - 85,000; Full time

Job summary

Winton Capital US LLC is seeking a Data Analyst for the Quantitative Platform to own data quality and cataloging as datasets move from onboarding to production. You will ensure datasets meet standards and are easily discoverable, covering vendor data, time series, and complex multi-table products where mapping matters as much as data accuracy.

You will collaborate with Strategy Managers, Data Engineers, and Quant Researchers to maintain data quality, address anomalies, and document limitations

Qualifications

• 3+ years' experience working with financial data vendors and their products.
• Strong grasp of cross-asset class time series data and onboarding datasets.
• Methodical approach to debugging inconsistencies in complex datasets.
• Hands-on analytical experience using Python and clear communication of findings to varied audiences.
• Meticulous attention to detail and bias toward evidence-based conclusions.
• Excellent communication and collaboration skills in a fast-moving, data-centric environment.

Responsibilities

• Defining and executing rigorous acceptance criteria for new and evolving data products, including coverage analysis, staleness, and gap detection.
• Acting as a subject-matter expert on data products, assisting with vendor formats, anomalies, and documentation gaps; tracking issues to resolution.
• Building and maintaining an automated catalogue of datasets with descriptions, owners, refresh cadence, SLAs, source systems, and schemas.
• Systematically probing datasets to ensure high data quality; stress-testing point-in-time, versioning, and revision semantics; addressing corrections and duplicates with vendors.
• Contributing to data quality frameworks, onboarding checklists, and documentation for repeatable quality audits.
• Partnering with Data Engineers, Quant Researchers, and operations on data handoffs, analytics sanity checks, and anomaly triage.

Skills

Python

Data quality

Data analysis

Communication

Teamwork

Tools

SQL

ETL

Job description

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; elevate 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 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.