Senior Quantitative Researcher/Scientist

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior Quantitative Researcher/Scientist based in India. This is a senior quantitative role focused on developing and governing the statistical methodology behind daily KPI indices and alternative-data-driven performance estimates. You will shape how complex data is transformed into robust, validated estimates that support trusted research and decision-making. The role combines advanced statistical modelling, forecasting, uncertainty quantification, data quality, and production engineering. You will take ownership of methodology across the full model lifecycle, from specification and estimation through validation, forecasting, publication, and revision. Working alongside experienced data scientists and software engineers, you will introduce stronger approaches for sparse, heterogeneous, and evolving data sources. Your expertise will directly influence the statistical quality and reliability of hundreds of published indices running on a daily basis. You will also raise statistical standards across the wider team through mentoring, training, methodological guidance, and thoughtful technical challenge.

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.

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