Current: Data Engineer, JPMorgan Chase · AWMPreviously NatWest Group, 2017 to 2024London

Experience

Nine years in banking risk and data

I started as a graduate analyst in data innovation, private banking and fraud, specialised in credit risk and stress testing, led model development, and now work in data engineering. Each move followed the same question: where does this decision process break, and how do I make it dependable?

Fig. 1 · Roles held, 2017 to presentJPMorgan Chase · NatWest Group

Data EngineerJPMorgan Chase

Lead Model DeveloperJPMC

Franchise LeadNatWest

Senior Quantitative AnalystNatWest

Graduate AnalystNatWest

Positions held

Roles and what changed because of them

Five roles across two banks, from analysis to model ownership to the data that models depend on.

Sep 2026
Present

Data Engineer

JPMorgan Chase · Chief Data & Analytics Office

Helping Asset & Wealth Management teams use critical data with confidence by making its source, meaning, quality and dependencies explicit.

Source, document and quality-check new datasets for analytics and AI use cases.
Trace and uplift lineage for critical data under BCBS 239, fixing defects at source and adding preventative controls.
Report data availability, dependencies and adoption to senior stakeholders.
Work across business, technology and operations to unblock difficult sources and close governance gaps.

Data provisioningLineageBCBS 239Data quality

Sep 2024
Sep 2026

Lead Model Developer

JPMorgan Chase · International Consumer Bank Risk

Built the loss forecasting process for a new and growing business within JPMorgan Chase.

Owned IFRS 9 and CECL model development, forecasting, governance and senior stakeholder communication.
Cleared seven-plus inherited model risk issues in the first year and embedded preventative controls.
Built a roll-rate model and automated forecasting, removing two FTE of recurring manual effort.
Led adoption of AWS SageMaker, CI/CD and GitHub across ICB Risk, upskilling more than 100 colleagues.
Presented provision, stress testing and data quality outcomes to the CRO and CFO. Received six peer-nominated awards for AI adoption and innovation.

IFRS 9CECLPythonSageMakerCI/CD

Jan 2023
Sep 2024

Franchise Lead

NatWest Group · UK Retail

Co-led BAU stress testing, loss forecasting and regulatory reporting for a roughly £200bn UK retail portfolio.

Directed stress test analysis and forecast challenge across mortgages and other retail products.
Designed a PySpark ETL pipeline to identify early mortgage forbearance, feeding live Tableau dashboards.
Migrated PD models from logistic regression to ARIMAX time series to improve macro sensitivity and stability.
Directed the SAS to Python rebuild of IFRS 9 staging flows, cutting runtime from one hour to ten minutes.

Stress testingPySparkARIMAXTableau

Sep 2019
Jan 2023

Senior Quantitative Analyst

NatWest Group · NatWest Markets and Ulster Bank

Analysed and challenged stress test outcomes, translating model behaviour into business impact.

Flagged data quality issues and portfolio vulnerabilities with clear mitigation actions.
Built automated Python, Excel and Tableau pipelines for portfolio analysis and model performance review.
Presented drivers, business implications and recommendations to senior management.

Credit riskPythonTableauExcel

Sep 2017
Sep 2019

Graduate Analytics Analyst

NatWest Group · Graduate Programme

Built breadth across innovation, private banking and fraud before specialising in quantitative risk.

Data Innovation & Strategy: machine learning work with pandas, NumPy and scikit-learn.
Coutts Private Banking: customer segmentation and competitor analysis using SAS, SQL and Tableau.
Fraud & Security Solutions: research and learning material on fraud detection and prevention.

SASSQLscikit-learnCustomer analytics

Skills

How the skills line up

Set out as a team sheet. Data foundations at the back, risk modelling in midfield, statistical methods up front. Each line depends on the one behind it.

MethodsRisk modellingData foundations

7Bayesian inference

9Monte Carlo simulation

11Machine learning

8Stress testing

6IFRS 9 and CECL

10Loss forecasting

2Metadata

5Data lineage

4BCBS 239

3Controls

1Data quality

Full toolkit

Risk & modelling: IFRS 9, CECL, ECL, PD / LGD / EAD, stress testing, roll-rate models, ARIMAX, VAR
Data engineering: PySpark ETL, lineage, metadata management, data quality, provisioning
Programming: Python, pandas, NumPy, scikit-learn, PySpark, XGBoost, SQL, SAS, VBA
Cloud & delivery: AWS SageMaker, Docker, Git, GitHub Actions, Bitbucket, CI/CD
Visualisation: Tableau, Power BI, Looker, Excel
Governance: BCBS 239, model risk management, regulatory reporting, control design

Education and recognition

Engineering first, then finance

The University of Manchester · 2013 to 2017

Materials Science & Engineering

Coursework in thermoelectric and nuclear materials, engineering mathematics, experimental analysis and materials characterisation. My dissertation examined the thermoelectric properties of lanthanum-doped strontium titanate.

6×

Peer awards

JPMorgan Chase: AI adoption, innovation and department-wide GitHub upskilling.

3×

LOV awards

NatWest: Stress Testing Analytics, Coutts Product Analytics and the Graduate Programme.