Postdoctoral Research Associate on Time Series Synthetic Data Generation

Contract Type - Fixed Term - 24 Months

Start date:  ASAP or by mutual agreeement

We are looking for an exceptional candidate to join the School of Mathematics at the University of Edinburgh to conduct research on the use of generative machine learning models and synthetic time series data with applications to Finance. 

This post is advertised as full-time (35 hours per week). We are open to considering requests for hybrid working (on a non-contractual basis) that combine a mix of remote and regular on-campus working.

The post is subject to Level 4 pre-employment screening - PES4 Enhanced Check

The Opportunity:

The project falls under a partnership between NatWest Group and the University of Edinburgh and seeks to develop tailor-made synthetic data generation that can be used for solving the following challenges: 

1. Model Risk for ML Systems: Machine learning applications in banking require thorough model risk analysis before being deployed into production. Performance testing and assessing the model's performance at edge cases, considering accuracy, fairness, and explainability metrics, are crucial. However, these assessments are often limited by the availability of historical data. By introducing synthetic data generation, we can enhance the performance analysis and address the limitations imposed by historical data scarcity.
2. Benchmark Data Sets: The bank currently lacks shareable benchmark data sets that provide appropriate privacy guarantees. This absence of standardized benchmark data sets results in lengthy and costly evaluation processes involving multiple data-sharing agreements when assessing commercial third-party ML solutions. Widely available benchmark data sets for various use cases can also enable the research community to systematically compare and evaluate novel ML solutions. 
3. Data Fluidity: The lack of high-quality private synthetic data hinders collaboration with external organizations, such as academics, as well as internal data science teams. By developing tailor-made synthetic data solutions, we can enable smoother collaboration and knowledge exchange with external stakeholders and internal teams, fostering innovation and advancements in the banking sector.

The objective of this project is to understand the appropriate balance between privacy, fidelity and utility of synthetic data for applications such as Credit Risk and Pricing. This will require the development of novel algorithms and approaches for (conditional) time-series data generation. The candidate will be working with Professor Lukasz Szpruch (University of Edinburgh and The Alan Turing Institute ) and Data Scientists at NatWest Banking. 

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