Dr William Peter Nicholson
Ph.D. Physics, University College London
Data Science and Machine Learning Expert
AWS Certified Cloud Practitioner
Google Cloud Digital Leader
IBM Machine Learning Professional
PRINCE2® Practitioner
Personal Profile
- Experienced data scientist and machine learning engineer, with over a decade of industry and academic leadership in AI system development, data-driven product strategy and full-stack deployment. Proven track record applying supervised, unsupervised, and deep learning models to high-impact problems across healthcare, logistics, and e-commerce. Strong production ML experience including Python server-side development, version control (Git), CI/CD automation (GitHub Actions, MLFlow), model deployment (Flask, Dash), monitoring (AWS), and automated data pipelines (PySpark, Fivetran, Snowflake).
- Sponsored University College London Ph.D. Physics graduate, Professional Member of BCS (The British Chartered Institute for IT), and PRINCE2® Practitioner.
- Team Leadership, project management, and cross-functional communication demonstrated across both start-ups and established companies, with experience serving as Head of Data Science.
- Driven to continually improve my core data science and system design skill sets, and broader business mindset through continuing professional certifications (AWS Cloud Practitioner, Google Cloud Digital Leader, IBM Machine Learning Professional) and advanced project leader certification (PRINCE2 Practitioner).
- Career focused on data science, machine learning, big data, and data visualization solutions; using my technical skills to extract the maximum knowledge and practical use from software and data. To deliver first rate programming and project management skills alongside a full understanding of how IT is integral to strategic decisions, profitability and functionality.
Professional Experience
Lead AI and Systems Engineer, Centers for Disease Control and Prevention, México City
09/2022-Present
- Project technical lead for the end-to-end design and implementation of a React/Remix full-stack antimicrobial resistance surveillance application, ensuring robust version control (Git) and applying service-oriented architecture principles, to support a national antimicrobial resistance strategy.
- Designed, developed, and deployed MongoDB Atlas Vector Search integrated with Prisma ORM for NoSQL data modelling to enable Retrieval-Augmented Generation (RAG) pipelines over clinical text and semi-structured patient records; enhancing the system’s ability to retrieve, synthesize, and summarize relevant data in real time.
- Containerized application components using Docker and orchestrated services with Kubernetes, ensuring consistency across deployment environments, scalable deployment, and efficient resource management across development and production.
- Delivered the first national-level primary healthcare tool, in México’s history, with real-time data visualization and embedded AI insights, enabling providers to record and monitor patient symptoms and treatments.
- Extensive close collaboration with federal and state level government officials, community leaders, and primary healthcare professionals nationwide to understand, design, and implement project requirements, on time and on budget.
Key achievements
Successfully delivered México’s first electronic health data collection and antimicrobial resistance tracking tool for primary healthcare. The project prompted direct engagement from the Mexican Ministry of Health to explore expanded deployment across broader healthcare settings.
Technologies used
MongoDB Atlas (including Vector Search), LangChain (RAG integration), Remix.js, ReactJS, TypeScript, HTML5, CSS3, NoSQL, Prisma ORM, Tailwind CSS, Git, Docker, Kubernetes, Ubuntu 22.04, Nginx.
Founder and Head of Data Science, Meru, México City
08/2020-04/2022
- Founding member and Head of Data Science of a Y-Combinator-backed e-commerce start-up, creating a first-of-its-kind cross-border wholesale B2B platform connecting Chinese manufacturers directly with Latin American retailers. Instrumental in securing our $15M USD Series A funds through demonstrable data-driven innovation.
- Designed and led the development of a modular, Docker containerized, AI-driven microservice architecture - featuring RESTful Flask APIs and AWS Lambda serverless functions - for dynamic, end-to-end pricing and full supply chain modelling; from Chinese factory floor to last-mile delivery in México.
- Developed and deployed containerized supervised learning models and unsupervised clustering algorithms to enable real-time pricing, logistics, and supply chain optimization
- Led a geographically distributed team of 10 data scientists across Latin America and China, delivering predictive models and logistics algorithms enabling accurate, real-time landed pricing.
- Championed adoption of MLOps best practices, including Git-based version control, CI/CD pipelines (MLFlow), automated data validation, and continuous model monitoring to ensure robust performance.
Key achievements
Created a first-in-market, AI-driven pricing engine and modularized supply chain modelling system; enabling scalable, transparent pricing across global B2B and B2C e-commerce logistics in Latin America.
Technologies used
AWS Lambda (serverless), RESTful Flask APIs, Python, Plotly/Dash, SQLAlchemy, Pandas, Scikit-Learn, Flask, SQL, Google Maps API, Snowflake, FiveTran, Oracle NetSuite, Tableau.
Senior Data Scientist, CHEP LATAM, México City
10/2018-08/2020
- Developed and deployed automated time-series forecasting models (ARIMA, SARIMAX, ETS) into AWS cloud-native production pipelines to automate supply and demand predictions across Latin American operations.
- Used Git for version control and GitHub Actions for CI/CD automation to deploy forecasting models and ensure consistent updates to AWS cloud-native environments.
- Replaced legacy manual forecasting workflows with fully automated data-driven pipelines delivering KPI time series forecasts to business stakeholders through interactive bulletins and a central internal analytics portal.
- Forecasts directly supported seasonal demand planning improving inventory allocation accuracy and logistics operations, and contributing to reduced operational friction across multiple regional markets.
- Partnered with cross-functional teams in logistics, operations, and finance to ensure models aligned with business needs and were integrated into decision-making cycles across the LATAM region.
Key achievements
Spearheaded the transition from manual demand planning to automated, model-driven forecasting; empowered colleagues across Latin America with data-driven insights to optimize resource allocation.
Technologies used
AWS, Python, Git, GitHub Actions, NumPy, Scikit-Learn, SQLAlchemy, Matplotlib, Seaborn, SQL.
Professor of Data Analytics, Tecnológico de Monterrey (for Trilogy Education Services), México City
01/2018-10/2018
- Designed and delivered in-person courses on data science, machine learning, and data visualization, teaching a wide range of industry-relevant technologies including Python, JavaScript, SQL, Hadoop, Tableau, R, and Git/GitHub.
- Developed full course syllabi, hands-on labs, and assessment strategies; emphasizing real-world project work, cross-disciplinary applications, and the use of modern data tooling.
- Led working groups of adult learners through end-to-end machine learning project cycles, fostering practical experience in model design, data wrangling, and results interpretation.
- Acted as both instructor and mentor, instilling best practices in data analysis and software engineering, and inspiring students to pursue careers in the data and AI fields.
- Advocated for a curriculum that blended foundational statistical understanding with modern coding fluency; preparing students for rapidly evolving industry expectations.
Key achievements
Established a hands-on, industry-aligned learning experience that equipped professionals with the practical skill set needed to succeed in real-world data science and analytics roles.
Technologies used
Git, Python, JavaScript, Hadoop, NumPy, Matplotlib, Seaborn.
Project Manager, Barclays Corporate Banking, London
08/2014-12/2017
- Led the design and execution of the pilot phase for the UK's first voice recognition system for telephone banking, introducing biometric authentication into live customer interactions.
- Oversaw the pilot's full lifecycle: from stakeholder negotiation and technical planning, to data acquisition, performance analysis (SQL, Python), and post-pilot evaluation and reporting.
- Coordinated across executive leadership, software engineering, network infrastructure, and frontline call centre teams to ensure technical feasibility, regulatory compliance, and smooth implementation.
- Acted as a key bridge between technical teams and business stakeholders, translating business goals into actionable development roadmaps and ensuring continuous alignment throughout the pilot.
- Supported the wider Barclays Corporate Bank technology portfolio, managing multiple additional projects focused on digital transformation and service modernization.
Key achievements
Successfully delivered the UK banking industry's first live deployment of voice recognition technology, paving the way for wider adoption of biometric solutions in customer service environments.
Technologies used
Python, Pandas, NumPy, Matplotlib, Seaborn, Microsoft Office tools.
Post-Doctoral Research Associate, University College London
01/2011-01/2013
- Awarded competitive European Union research funding to lead a cross-institutional scientific software development project in collaboration with the Laboratoire de Planétologie de Grenoble, France.
- Designed, developed, and deployed a fully operational online climate and atmospheric simulation platform, integrating physics-based models of planetary atmospheres, and building front-end interfaces with HTML5, CSS3, and JavaScript, alongside backend services using Node.js and Python.
- Acted as both lead developer and scientific researcher, navigating interdisciplinary collaboration and technical delivery in a high-stakes, grant-funded environment.
Key achievements
Delivered a production-grade scientific simulation platform used in international planetary science research. Demonstrated both advanced software engineering and domain-specific modelling expertise.
Technologies used
Python, Pandas, NumPy, IDL, FORTRAN, JavaScript, Node.js.
Contact
Education
Ph.D. Physics
University College London
2005-2010
MSc Space Science
University College London
2004-2005
BSc Astrophysics
University of Edinburgh
1999-2003
Publications
- Nicholson, W.P., Ph.D. Thesis, 2011
- Nicholson, W.P., et. al., Mon. Not. R. Astron. Soc., 400, 369-382, 2009
- Moffat-Griffin, T., A.D. Aylward, W.P. Nicholson, Ann. Geophys., 28, 2147-2158, 2007
Professional Qualifications and Memberships
Google Cloud Digital Leader
Google Cloud
September 2022
AWS Cloud Technical Essentials
Amazon Web Services
June 2022
Building a Data Science Team
John Hopkins University
May 2022
IBM Machine Learning Professional Certificate
IBM
May 2022
Professional Membership of BCS, the Chartered Institute for IT
BCS, the Chartered Institute for IT
May 2016
PRINCE2® Practitioner in Project Management
Certificate number 00261331, candidate number NH34065354
April 2016
PRINCE2® Foundation in Project Management
certificate number 00198850, candidate number YV32998610
September 2015
Awarded Certificate in IT (Ofqual level 4 qualification)
BCS, the Chartered Institute for IT
Apr 2014
Object-Oriented Programming Using C++, C# in Detail
City University, London, UK
2013
Awarded sponsorship
London Business School, London, UK
October 2007
Languages
- English: Native speaker
- Spanish: Intermediate
Extracurricular Activities
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Charitable activities (2004)
- 612km/7-day bike ride for Macmillan Cancer Relief raising £3500.
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Civil Aviation Authority pilot flight training (2002)
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SCUBA diving training (2001)
- PADI Open Water Diver; PADI Advanced Open Water Diver.
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University Student Council member (2002-2003)
- Edinburgh University Science and Engineering faculty representative.