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About the Role

This role is a new, dedicated senior position focused on advancing the statistical methodology behind daily KPI indices. You will be responsible for transforming alternative data into robust estimates of company performance, ensuring models are specified, validated, and governed before publication. The position requires introducing deeper statistical modeling, stronger uncertainty quantification, and robust approaches for handling sparse and heterogeneous data.

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 KPI models.
Own the adjustment and drift-control methodology, including scaling, bounds, clipping, treatment of outstanding corrections, and selection/evidence of thresholds and windows.
Own the statistical treatment of panel and source data before modeling, including coverage, reporting lag, sample drift, and representativeness.
Set standards for the model lifecycle, including specification, validation, backtesting, retraining, and evaluation using only information available at publication time.
Own the revision methodology for published series, including justification for restatement and preservation of historical consistency.
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 for engineers and data scientists, 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 data scientists on methodology, especially new methods.
Mentor data scientists and raise statistical standards across the wider team.

Requirements

5+ years of work experience in applied statistics, econometrics or quantitative data science or a PhD in a relevant field.
Demonstrated experience owning statistical methodology in a production system where modeling decisions reached external users.
Postgraduate training in statistics, econometrics or another quantitative discipline, or equivalent applied experience.
Deep understanding of regression modeling and statistical inference, including diagnostics, constrained estimation and uncertainty estimation.
Strong time-series expertise, including forecasting, seasonality, calendar effects, structural breaks and time-series cross-validation.
Strong understanding and experience with two or more of the following: Bayesian modelling (particularly hierarchical models), Kalman filtering, Errors-in-variables models, Change point detection, Conformal prediction, Forecast combination (e.g. Bates and Granger method).
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 methodology under production constraints such as data revisions, missing inputs, short histories and backward compatibility of published series.
Fluent Python with the statistical stack, including statsmodels, scipy, pandas and NumPy.
Comfortable contributing to a large shared production codebase using version control, code review and automated testing.
Experience writing statistical code that runs unattended, with appropriate validation, reproducibility and failure handling.
Strong SQL skills.
Excellent written communication and the ability to explain statistical reasoning clearly to non-statistical audiences.
Comfortable acting as the methodological authority within a multidisciplinary team and challenging weak approaches with clear reasoning.
Fluent in English.

Skills

Applied statistics
Econometrics
Quantitative data science
Statistical methodology
KPI indices
Alternative data
Model specification
Model validation
Model governance
Statistical modeling
Uncertainty quantification
Sparse data handling
Heterogeneous data handling
Regression modeling
Statistical inference
Time-series analysis
Forecasting
Seasonality
Calendar effects
Structural breaks
Time-series cross-validation
Bayesian modeling
Hierarchical models
Kalman filtering
Errors-in-variables models
Change point detection
Conformal prediction
Forecast combination
Sampling methodology
Panel methodology
Selection bias
Unbalanced panels
Reweighting
Representativeness
Automated decision systems
Data revisions
Missing inputs
Backward compatibility
Python
statsmodels
scipy
pandas
NumPy
Version control
Code review
Automated testing
SQL
Written communication
Methodological authority
Alternative data
Nowcasting
KPI estimation
Transaction data
Web data
App data
Uncertainty quantification for time series
Prediction intervals
Conformal methods
Adaptive conformal methods
Index construction methodology
Chaining
Rebasing

Experience Level

Senior