Senior Quantitative Researcher/Scientist

Accountabilities:

• Lead the statistical methodology for index production end to end, covering model specification, estimation, adjustment, validation, forecasting, and ongoing governance.
• Develop substantial methodological improvements that enhance the quality, robustness, and predictive performance of KPI models.
• Own adjustment and drift-control methodology, including scaling, bounds, clipping, outstanding corrections, and the selection and evidence behind relevant thresholds and time windows.
• Define the statistical treatment of panel and source data before modelling, including coverage, reporting lag, sample drift, selection effects, and representativeness.
• Establish standards for the complete model lifecycle, including specification, validation, backtesting, retraining, and evaluation using only information available at the time of publication.
• Own revision methodology for published series, determining when restatement is statistically justified and ensuring appropriate historical consistency.
• Define the statistical logic behind production quality assurance, including input monitoring, outlier detection, alert calibration, and the distinction between data issues and model issues.
• Maintain clear and rigorous methodology specifications that engineers and data scientists can implement consistently.
• Review methodological changes proposed by product teams and provide statistical guidance on their suitability and implications.
• Validate numerical equivalence and statistical correctness when libraries or scientific dependencies are upgraded.
• Implement statistical methodology as tested, production-quality Python and take accountability for the correctness of published outputs.
• Deliver training sessions to other data scientists, particularly on newly developed methods and statistical approaches.
• Mentor data scientists and contribute to raising statistical standards across the wider multidisciplinary team.

Requirements

• 5+ years of professional experience in applied statistics, econometrics, quantitative data science, or a related field, or a PhD in a relevant discipline.
• Demonstrated experience owning statistical methodology in a production environment where modelling decisions directly influence externally used outputs.
• Postgraduate training in statistics, econometrics, or another quantitative discipline, or equivalent applied experience.
• Deep understanding of regression modelling and statistical inference, including diagnostics, constrained estimation, and uncertainty estimation.
• Strong time-series expertise covering forecasting, seasonality, calendar effects, structural breaks, and time-series cross-validation.
• Strong practical knowledge of at least two advanced methodologies such as Bayesian or hierarchical modelling, Kalman filtering, errors-in-variables models, change point detection, conformal prediction, or forecast combination.
• Experience with sampling and panel methodology, including selection bias, unbalanced panels, reweighting, and representativeness.
• Experience calibrating thresholds and uncertainty measures for automated decision systems, with sound judgment around false positives and missed errors.
• Ability to reason about statistical methodology under production constraints, including data revisions, missing inputs, short histories, and backward compatibility of published series.
• Fluent Python skills with statistical libraries including statsmodels, SciPy, pandas, and NumPy.
• Comfortable contributing to a large shared production codebase using version control, code review, automated testing, and reproducible development practices.
• Experience writing statistical code that runs unattended with appropriate validation, reproducibility, monitoring, and failure handling.
• Strong SQL skills for working with analytical datasets and production data pipelines.
• Excellent written communication skills, with the ability to explain complex statistical reasoning clearly to non-statistical audiences.
• Comfortable serving as a methodological authority within a multidisciplinary team and challenging weak approaches through clear, evidence-based reasoning.
• Fluent in English.
• Experience with alternative data, nowcasting, or KPI/revenue estimation using transaction, web, app, or similar data sources is desirable.
• Experience with time-series uncertainty quantification, including prediction intervals, conformal prediction, or adaptive conformal methods, is beneficial.
• Familiarity with advanced approaches such as state-space models, hierarchical or pooled modelling, forecast reconciliation, or related statistical methods is a plus.
• Experience with index construction methodologies, including chaining and rebasing, is desirable.

Benefits

• Fully remote position within a technology-focused, multidisciplinary team.
• Senior-level ownership over statistical methodology used in daily production processes.
• Opportunity to work on challenging problems involving alternative data, KPI estimation, forecasting, uncertainty, and data quality.
• Collaboration with experienced data scientists and software engineers across a production analytics environment.
• Opportunity to introduce and implement advanced statistical methodologies and directly influence the quality of published outputs.
• Scope to shape model lifecycle standards, validation practices, revision methodology, and production quality assurance.
• Opportunity to mentor data scientists and raise statistical standards across the wider team.
• Environment that supports autonomy, rigorous quantitative thinking, technical ownership, and continuous learning.
• Exposure to large-scale, continuously updated datasets and statistical systems operating across hundreds of published indices.