Senior Quantitative Researcher

We are looking for a Senior Quantitative Researcher to take a leading role in developing and governing the statistical methodology behind our daily KPI indices.

Our methodology determines how alternative data is transformed into robust estimates of company performance, including how models are specified, validated, and governed before publication. It is developed and maintained by the OXDS team and runs every day across several hundred published indices. This is a new, dedicated senior position created to advance that methodology, introducing deeper statistical modelling, stronger uncertainty quantification, more robust approaches for sparse and heterogeneous data, and methods that continue to perform as our data sources and products evolve.

In this role, you will be the senior statistical voice for the index production system, working within the OXDS team alongside software engineers and with data scientists across the product teams.

Roles and Responsibilities

• Lead the statistical methodology of index production end to end: model specification, estimation, adjustment, validation, and forecasting.
• Propose and develop substantial methodological enhancements to improve the quality of our KPI models beyond our existing approaches.
• Own the adjustment and drift-control methodology: scaling, bounds, clipping, and the treatment of outstanding corrections, including how thresholds and windows are selected and evidenced.
• Own the statistical treatment of panel and source data before modelling, including coverage, reporting lag, sample drift, and representativeness.
• Set standards for the model lifecycle, including specification, validation, backtesting, retraining, and evaluation using only the information available at the time of publication.
• Own the revision methodology for published series, including when restatement is justified and what historical consistency it should preserve.
• Own the statistical logic behind production quality assurance, including input monitoring, outlier detection, alert calibration, and distinguishing data issues from model issues.
• Maintain clear methodology specifications that engineers and data scientists can build against, and review methodological changes proposed by product teams.
• Validate numerical equivalence when statistical libraries or scientific dependencies are upgraded.
• Implement methodology as tested, production-quality Python and take accountability for the statistical correctness of published outputs.
• Deliver training sessions to other data scientists to upskill them in our methodology, especially for new methods you have developed.
• Mentor data scientists and raise statistical standards across the wider team.