Geoff Nightingale: Full Stack Data Scientist
Experienced data scientist with a proven track record across the Finance, Technology, Insurance, and Energy sectors. I bring a broad,
end-to-end skill set spanning Data Engineering, Data Science and Machine Learning Engineering. I combine strong technical expertise
with excellent stakeholder engagement, a commercially focused mindset, and a consistent history of delivering measurable business
value.
Skills
- Programming Languages: Python, SQL, C#, Dart
- DS / ML Libraries: Numpy, Pandas, ScitkitLearn, PyTorch, Tensorflow, Keras, plotly, matplotlib
- Big data and databases: Snowflake, DBT, Databricks, PySpark, Oracle, Databricks
- Data Viz: Looker, Streamlit, Plotly, Matplotlib, Seaborn
- DevOps and MLOps: Git, Github Actions, Docker, Terraform, Airflow, MLFlow, Flask, FastAPI
- Cloud Platforms: GCP, AWS, Azure
Experience
Data Science Lead - Zilch, London | Jul 2024 - Present
At Zilch, I lead AI and ML development across the Credit Risk, Fraud, and Collections domains.
- Defined and delivered the model development roadmap across these areas and regularly presented progress and outcomes to C-suite stakeholders.
- Developed core models including transaction fraud detection, probability of default, and propensity-to-pay, and advised the business on their practical application in decisioning and strategy.
- Translated random forest outputs into production-ready fraud rules, contributing to a reduction in fraud of over 10%.
- Oversaw the development of application scorecards and their use within credit strategy, including decline rules, risk banding, and product allocation.
- Designed the methodology for assigning initial customer credit limits and built the affordability-driven credit limit increase logic, enabling over £10M in monthly credit limit uplifts.
- Led enhancements to model monitoring dashboards and worked closely with senior stakeholders including the Chief Product Officer and Chief Risk Officer.
Senior Consultant - Contino, London | Apr 2022 - Jul 2024
Technical delivery of Data Science and Data Engineering projects across client engagements.
Energy Company:
- Built time-series ML models in Python using recurrent neural networks to forecast reservoir water
levels over a 24-hour period to manage flood risk and power generation. Models accurate to within an
average of +/-0.5 ft over the forecast window.
- Collected and cleansed data, and merged with third party climate data sources.
- Presented findings to senior business stakeholders.
Insurance Company:
- Designed ML feature stores using Snowflake and Feast for Data Science model development,
inference and monitoring.
- Developed Python tools to automate code generation from JSON config files, saving hundreds of
hours writing code manually.
- Translated business processes from SAS to Oracle SQL. Optimised queries to reduce monthly runtimes
by over 1,000 hours per month.
- Mentored team members on Git and SQL best practices.
- Prepared and presented educational sessions on ML topics such as MLOps and Large Language Models to
the wider organization.
Data Science Manager - Vanquis Bank, London | Dec 2017 - Apr 2022
Hands-on manager responsible for leading Machine Learning projects across the bank.
- Redeveloped SAS scorecard development tools using Python and MLOps frameworks reducing scorecard
development time from days to < 5 minutes.
- Built machine learning models across the business including:
- Application Fraud model using gradient boosting. Results from A/B testing indicate reduced fraud
losses of £278k per year.
- Collections contact model to predict the best time to call customers, increasing right party contact
rates by 25%.
- Application of NLP methods to categorise CRM notes from customer interactions, providing valuable
insight to Operations.
- Introduced SWE best practices (Git, unit/integration testing, CI/CD etc.).
- Redesigned ML model monitoring reports using modern BI tools to automate report updates in sync with
changes to source data.
- Lead and mentored a team of 3 Data Scientists.
- Prepared and presented workshops to introduce ML concepts and the use of Git across the organisation.
Senior Consultant - Euristix, London | Sep 2016 - Dec 2017
Responsible for leading model development and data science projects.
- Developed IFRS9 model using Markov decision process to forecast expected credit losses.
- Developed debt pricing models using decision trees and regression models to provide pricing estimates to debt collection agencies.
- Lead and mentored a team of 3 analysts, managed objectives and career progression.
Credit Risk Manager - ANZ, Auckland | Jun 2013 - Sep 2016
Optimisation of new business and existing customer credit risk strategies using quantitative methods.
- Developed a new credit limit assignment strategy using decision trees and k-means clustering. This work was expected to generate a $1M p.a.
profit uplift and one-off capital savings of $3.5M.
- Analysed and improved policy rule set for credit cards increasing approval rates from 73% to 80%.
- Developed profitability model for credit cards utilizing transaction level data. Model was used to design score cut offs and limit assignment
strategies to achieve target RoE of 25%.
Credit Risk Analyst/Graduate Analyst - GE Capital, Auckland | Apr 2010 – Jun 2013
Responsible for the optimisation of credit risk collections strategy for credit cards and personal loans.
- Used linear regression to predict total payments to be recovered on charged-off debt in the following 12 months. This model was used to rank
and prioritise collection accounts. A/B testing results showed a recoveries uplift of 22% over 6 months when using this model vs control.
- Responsible for producing monthly Credit Scorecard monitoring reports for Credit Cards.
- Conducted A/B testing of various contact channel combinations in order to formulate improved contact strategies.
Education & Certifications
MSc Artificial Intelligence (Distinction) - University of Bath | 2020-2023
BSc Economics & Finance (Honours) - University of Waikato | 2005-2009
Deployment of ML Models - Udemy | 2021
Deep Learning Specialization - Coursera | 2019
Machine Learning - Coursera | 2015
Projects
Financial Projects
Health Projects