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GCP · Python · Data Engineering · Finance

Jack
Gough

// BSc Computer Science — University of Birmingham
// Data Engineering · Statistical Modelling · Warranty & Cost Analysis

CS student with a full year at Jaguar Land Rover — building GCP data pipelines, Python signal analysers, BEV battery degradation models, and financial warranty cost models used at OEM scale. The technical edge that finance needs.

View My Work Get In Touch
Placement
JLR
Fin. & Warranty Modelling
Degree
BSc CS
University of Birmingham
Core Skill
Python
Data Analysis & Automation
Availability
2027
Graduate Roles Open
CS Graduate · Data & Finance
GCP pipelines · MF4 signal analysis · BEV modelling · Warranty forecasting
01 / About

Built in Industry, Not Just a Classroom

A year at JLR gave me something most CS graduates don't have — real data engineering and financial modelling experience at OEM scale.

Background

I'm Jack Gough, a Computer Science student at the University of Birmingham, targeting roles in finance and data engineering where technical depth is a real differentiator.

During my placement at Jaguar Land Rover, I built a GCP data pipeline from propulsion test rigs through to Tableau KPI dashboards, designed a Python MF4 file analyser now deployed across the business, modelled BEV battery degradation from drive cycle data to forecast warranty costs, and produced financial cost models used in real programme budget decisions.

I write the code, build the model, and understand the business problem behind it — a combination that pure finance or pure CS graduates rarely offer.

Technical Skills
Python / pandas / NumPy
GCP / BigQuery / DQL
Tableau / Data Viz
Statistical Modelling
Financial / Warranty Modelling
Signal Processing / MF4
SQL / Databases
Excel & Automation
Education

University of Birmingham
BSc Computer Science
2022 — Present

Relevant modules: Data Structures & Algorithms, Machine Learning, Databases, Mathematical Foundations of CS, Software Engineering

PLACEMENT YEAR · 2025–2026
Jaguar Land Rover
Propulsion System Analysis · Warranty Analysis · Data Engineering
// 01 — GCP Pipeline
Test Rig Data to Live KPI Dashboards
Built a full pipeline from JLR propulsion test rigs through to Tableau — filtering and cleaning raw log files in Python, writing DQL scripts to build BigQuery views in GCP, then creating dashboards with statistical models tracking KPI failure rates across propulsion testing.
Stack
GCP · Tableau
// 02 — Signal Analysis
MF4 Analyser — Deployed Across JLR
Designed a Python tool to extract signals from raw MF4 measurement files, calculate statistical bounds, and flag anomalies. Now used across the JLR business — and kept propulsion test analysis running during a major cyberattack when all other workflows were taken offline.
Status
Live at JLR
// 03 — BEV Modelling
Drive Cycles → Battery Degradation → Warranty Cost
Categorised real-world drive cycles from vehicle data using Python clustering, then modelled battery degradation per profile. The outputs directly fed warranty cost estimates — predicting how many BEVs would fail within 6 years and what the financial liability would be.
Horizon
6yr Forecast
// 04 — Financial Models
Warranty Risk & Programme Cost Forecasting
Built warranty liability and programme cost models used in real JLR budget reviews — quantifying risk exposure by vehicle line, tracking actuals vs. forecast, and giving finance and engineering teams a structured view of cost risk before it crystallised.
Scope
Multi-Programme
SCROLL TO EXPLORE
⚠ Chart data is illustrative only.
Values shown are not real JLR figures.
500 400 300 200 100 JAN FEB MAR APR MAY JUN JUL BUDGET LINE ⚠ WARRANTY RISK BAND £1.8M – £2.4M EXPOSURE ⚡ AUTO-REPORT GENERATED DELTA vs FORECAST: -4.2% CONFIDENCE: 94.1% PROGRAMME COST PERFORMANCE — INDEX JLR PROPULSION · FINANCIAL MODELLING OUTPUT
02 / Experience

Where I've Worked & Studied

Industry Placement · Finance Focus
Jaguar Land Rover
// Propulsion Test Modelling — Financial & Data Engineering
Sept 2023 — Aug 2024
  • Built an end-to-end GCP data pipeline from propulsion test rigs — filtering and cleaning raw log files, writing DQL scripts to create structured BigQuery views, and building Tableau dashboards with statistical KPI failure models used by engineering and management.
  • Designed a Python MF4 file analyser now deployed across the JLR business — extracts signals, calculates statistical bounds, and flags anomalies. Proved critical during a major cyberattack when standard workflows were inaccessible.
  • Used Python clustering to categorise drive cycles from real BEV vehicle data, then modelled battery degradation per profile to estimate warranty failure rates and costs over a 6-year horizon.
  • Built warranty and programme cost models in Excel and Python — quantifying risk exposure by vehicle line, tracking actuals vs. forecast, and feeding cross-functional budget reviews.
  • Analysed functional safety testing data and built statistical models to surface failure patterns and support safety case documentation.
Education
University of Birmingham
// BSc Computer Science
Sept 2022 — Present
  • Studying core CS disciplines providing the quantitative and analytical foundation highly valued in finance: algorithms, mathematical logic, statistics, and ML.
  • Modules in database systems and SQL directly applicable to financial data management, reporting, and analysis at scale.
  • Software engineering coursework — agile practices, version control, code review — mirrors the structured, auditable approach required in regulated finance environments.
  • Completed a full industrial placement year, gaining financial modelling experience that most CS and finance graduates don't reach until years into their careers.
  • Final year project applying data science and machine learning techniques to real-world analytical problems.
03 / Work & Projects

What I've Built

GCP pipelines, Python signal tools, BEV degradation models, and financial forecasting — all built at JLR during my placement year.

Data Engineering · GCP · Tableau · SQL
Test Rig → GCP → Tableau KPI Pipeline
Built an end-to-end data pipeline connecting JLR propulsion test rigs to Google Cloud Platform and Tableau. Involved filtering and cleaning raw log files from test rigs, writing DQL scripts to create structured views in GCP, then building Tableau dashboards with statistical models tracking KPI failure rates across propulsion testing programmes. Gave engineering and management a live view of test quality metrics that previously required manual extraction.
GCPTableauSQL / DQLData PipelineLog ParsingKPI ModellingPython
Signal Processing · Python · Business-Wide Tool
MF4 File Analyser ↗ Used across JLR
Designed and built a Python-based MF4 file analyser now deployed across the JLR business. Extracts signals from raw measurement files, calculates statistical bounds, and surfaces anomalies — all without needing specialist tooling. Proved critical during a major cyberattack when standard workflows were inaccessible, keeping propulsion testing analysis running.
Screenshot coming soon — swap the <img> src below
PythonasammdfSignal BoundsMF4pandasCyberattack Resilience
Machine Learning · BEV · Warranty Modelling
Drive Cycle Categorisation & Battery Degradation
Used Python to categorise real-world drive cycles from existing vehicle data, then modelled battery degradation in new BEV models based on those profiles. The outputs directly fed warranty cost estimates — predicting failure rates over 6 years to inform financial provisioning for the EV warranty book.
PythonClusteringBEVBattery DegradationWarranty Forecasting
Safety Analysis · Statistical Modelling
Functional Safety Testing Analysis
Analysed results from functional safety testing programmes and built statistical models from the data. Surfaced patterns in failure modes and test outcomes to support safety case documentation and engineering decision-making.
Statistical ModellingSafety AnalysisPythonData Visualisation
Financial Modelling · Cost Forecasting · Excel
Programme Cost, Warranty & Financial Modelling
Produced financial and warranty cost models across multiple JLR programmes — forecasting expenditure, quantifying warranty risk exposure by vehicle line, and providing structured outputs for cross-functional budget reviews. Combining engineering data with financial modelling to produce cost forecasts that directly influenced programme decisions.
Financial ModellingExcelWarranty RiskCost ForecastingRisk QuantificationPython
04 / Deep Dives

Project Walkthroughs

01 / 04 ⚠ Figures and data shown are illustrative examples only — not real JLR data
05 / Contact

Let's Talk

Open to
opportunities.

I'm actively targeting graduate roles in finance, data engineering, and technical analysis. I bring a year of real OEM-scale experience — GCP pipelines, Python tooling deployed across a business, BEV financial modelling, and warranty cost forecasting. Let's talk.

Send Me an Email →
[email protected] linkedin.com/in/jack-gough1 Download CV
Available for graduate finance roles — 2027/28