Publication
From Solving Equations to Deep Learning: A TensorFlow Python Tutorial
https://www.toptal.com/developers/machine-learning/tensorflow-python-tutorial
Oliver is available for hire
Hire OliverOliver Holloway
Verified Expert in Engineering
Python Developer
London, United Kingdom
Toptal member since May 10, 2016
Bio
Oliver is a versatile data scientist and software engineer, combining over a decade of experience and a postgraduate mathematics degree from Oxford. His career assignments have ranged from building machine learning solutions for startups to leading project teams and handling vast amounts of data at Goldman Sachs. With this background, Oliver is adept at picking up new skills quickly to deliver robust solutions to the most demanding of businesses.
Portfolio
European-based Investment Company
Deep Learning, Kubernetes, Docker, ETL, Flask, SQL, Google Cloud Platform (GCP)...
Publicis Sapient
LangChain, BigQuery, Large Language Models (LLMs), Vertex AI, Hugging Face...
Patient Vox
Artificial Intelligence (AI), Machine Learning, Speech Recognition...
Experience
- SQL - 15 years
- Python - 10 years
- Google Cloud Platform (GCP) - 6 years
- Machine Learning - 4 years
- Pandas - 4 years
- Data Science - 3 years
- Natural Language Processing (NLP) - 3 years
- Large Language Models (LLMs) - 3 years
Preferred Environment
Git, PyCharm, Jupyter, Windows, Linux
The most amazing...
...application I've worked on allowed traders to visualize historical financial data, perform technical analysis and determine the relative value of securities.
Work Experience
Technical Lead: AI Evaluation & Backtesting
2019 - PRESENT
European-based Investment Company
- Led a team of Toptal engineers in a project to create a research platform for configuring and testing models for building investment portfolios.
- Researched architectures for portfolio-build models, including those using neural network approximation.
- Architected a high-concurrency Model Evaluation Factory on GCP and Kubernetes, parallelizing thousands of stress tests through worker queues to ensure statistical robustness and performance validation.
- Led the design of a scalable ETL and data harmonization framework, enabling the ingestion of fragmented data sources into a centralized, audit-ready database.
- Ensured rich reporting of model performance via user-defined metrics and information around portfolio constituents. This was directly consumable via an API, including a Dash and Plotly dashboard with interactive graphics.
- Reviewed and maintained code for the Angular web app through which an administrator can test models and view tabular reports.
- Designed the database model used by the ORM (SQLAlchemy), which also underpinned the RESTful API that was robustly marshaled using Marshmallow.
- Set up the Google Cloud Platform-based infrastructure, including a Docker-based CI/CD pipeline for both the UI and back-end service, a Kubernetes engine for parallelizing long-running worker processes, worker queues (Pub/Sub), and scheduled tasks.
- Built and experimented with an agentic AI researcher to run backtests and refine models using LangGraph, LangSmith, and MCP. Stored research steps and reasoning in the knowledge graph.
Technologies: Deep Learning, Kubernetes, Docker, ETL, Flask, SQL, Google Cloud Platform (GCP), Python, Neural Networks, Data Science, Machine Learning, Flask-RESTful, PostgreSQL, SQLAlchemy, Marshmallow, Angular, Keras, TensorFlow, Pandas, Alembic, Dash, Plotly, Artificial Intelligence (AI), APIs, Architecture, Full-stack, Technical Leadership, Software Architecture, Databases, Finance, Solution Architecture, AI Programming, Data Visualization, Data Modeling, Forecasting, Machine Learning Operations (MLOps), Google Cloud, Data Analysis, Research, System Architecture, Decision Trees, XGBoost, Agentic AI, LangGraph, LangSmith, Knowledge Graphs, LLM Reasoning, AI Project Management, Project Management, Team Leadership, System Design, Multi-agent Systems, Ray, Containers
Lead AI Solutions Architect (Financial Services)
2024 - 2025
Publicis Sapient
- Researched and applied large language models (LLMs) in the innovation team at a European Investment Bank client of Publicis Sapient, in collaboration with Google.
- Created a generic LangChain technique for multi-label classification of news articles using prompts to various LLMs and Vertex AI text embeddings.
- Managed multiple competing stakeholders, including travel to the company headquarters to present to senior management in person.
Technologies: LangChain, BigQuery, Large Language Models (LLMs), Vertex AI, Hugging Face, RAG Systems
AI/ML Engineer
2023 - 2023
Patient Vox
- Consulted on directions that a potential MVP could take for a diagnostic system in the mental health space.
- Researched and assessed transferable speech and text models using Hugging Face that could be fine-tuned and used in diagnosis.
- Demonstrated PoCs using IBM Watson and OpenAI's ChatGPT API (via LangChain) as a basis for a chatbot-based solution.
Technologies: Artificial Intelligence (AI), Machine Learning, Speech Recognition, Voice Analysis, Early-stage Startups, OpenAI GPT-3 API, Natural Language Processing (NLP), Chatbots, IBM Watson, AI Programming, Generative Pre-trained Transformers (GPT), Hugging Face, Language Models, LangChain, Large Language Models (LLMs), Research
Computer Vision Engineer
2021 - 2021
UK-based eCommerce Company
- Devised and implemented a system for receiving an image via API and finding the closest matching products in a catalog.
- Isolated the encoder of the MobileNet neural network architecture to translate images to compressed vectors which could then be queried using an efficient similarity search index.
- Used MLflow to manage model storage, experiment tracking, and serve the model through an API.
- Derived an autoencoder so that the encoder model could be fine-tuned with any available training data.
- Leveraged TensorFlow datasets to move image data through the pipeline without overwhelming memory.
- Created a Python package for consuming RabbitMQ messages to create and wait on multiple subprocesses, all performed in an asynchronous manner using asyncio.
Technologies: Python, TensorFlow, Asyncio, MLflow, Image Processing, Scikit-learn, Artificial Intelligence (AI), APIs, Computer Vision, Software Architecture, Architecture, AI Programming, Machine Learning Operations (MLOps), Machine Learning, Deep Neural Networks (DNNs), Data Analysis, Containers
Freelance Speech Recognition Engineer
2019 - 2019
US-based Tech Startup
- Created speech recognition models using custom neural networks with Keras, along with Baidu's DeepSpeech architecture via TensorFlow.
- Handled preprocessing of audio file input types (PCM/WAV), including conversions to and manipulations of MFCCs/spectrograms.
- Wrapped up processes for data uploading, model training, persistence, and inference in RESTful APIs, with capabilities for complex versioning and live updates of training.
- Deployed and configured APIs to leverage GPUs for faster training/inference and a Celery distributed-task queue for training in parallel.
Technologies: Deep Learning, Audio Processing, Data Science, Neural Networks, Machine Learning, MongoDB, Flask, Celery, Keras, Python, Artificial Intelligence (AI), APIs, Convolutional Neural Networks (CNNs), AI Programming, NVIDIA CUDA, Deep Neural Networks (DNNs), Research
Freelance Data Scientist
2019 - 2019
Kalepa Corporation
- Trained and evaluated various models for classifying text data using GloVe word representations, bag-of-words model, and XGBoost, among other ML/NLP techniques.
- Created and managed Amazon Mechanical Turk tasks for deriving labels for training classification, including detecting consistently inaccurate workers.
- Productionized the inference process into the pip installable Python package.
Technologies: Natural Language Processing (NLP), Generative Pre-trained Transformers (GPT), Machine Learning, Data Science, SpaCy, MongoDB, Scikit-learn, XGBoost, Gensim, Pandas, Python, Artificial Intelligence (AI), APIs, Social Media, GloVe, Data Modeling, Data Augmentation, Data Analysis, Research
Interim Chief Information Officer
2019 - 2019
Lawli Ltd
- Led the technical direction for a UK government-funded legal AI app to help deliver the initial version of the product.
- Implemented initial NLP solutions in Python for providing document services at the heart of the app.
- Created the first version of an Angular 7 web app using Node.js and MongoDB.
- Conducted interviews to find people for the company's first development team to continue my work.
Technologies: Generative Pre-trained Transformers (GPT), Natural Language Processing (NLP), WordPress, RabbitMQ, Gensim, Python, Node.js, Angular, Artificial Intelligence (AI), Full-stack, Software Architecture, Architecture, Machine Learning, CTO
Freelance NLP Expert
2018 - 2018
Zugata
- Improved and developed a system for key-phrase extraction from texts using a trained ML classifier and various extraction techniques (including those involving the statistical analysis of word collocations).
- Innovated a library that applies a dependency parser (including spaCy or the Stanford Parser) to texts and then extracts phrases according to grammatical rules automatically inferred from training texts (using graph theory and NetworkX).
- Implemented frameworks that helped with research, including the use of caches for extracted phrases and objects for persisting models with metadata to give consumer knowledge of how that model was formed.
- Created a Flask API for the output of results and the user's ability to specify the different methodologies.
- Enhanced an in-house evaluator of extractor performance accompanied by the integration of traditional evaluators (Bleu/Rouge); also, set up cross-validation tests for classifier performance.
- Verified and advised on statistical/confidence tests for studies by a company that went toward a paper that won an award at KDD 2018.
Technologies: SQL, Graph Theory, Generative Pre-trained Transformers (GPT), Natural Language Processing (NLP), Data Science, Machine Learning, NumPy, Pandas, MySQL, Flask, Scikit-learn, SpaCy, Natural Language Toolkit (NLTK), Python, Artificial Intelligence (AI), APIs, Data Modeling, Research
Freelance Data Scientist | Freelance Machine Learning Specialist
2017 - 2018
US-based Investment Management Firm
- Researched and tested prediction models with a Python stack using machine learning regressors and natural language processing techniques.
- Derived features from various sources, including forming vector representations of words/documents using a bag-of-words model (with NLTK) and neural networks (with TensorFlow).
- Developed a configurable model backtesting (and backfilling) system using various Pandas functionality extensively.
- Improved the reliability of a Selenium-based framework for scraping websites to source data for model training, including improved logging and reports of nightly performance.
- Created a framework for mining and structuring data from particular sections of PDF files.
- Enhanced and bug-fixed a React and Redux web app used for showing predictions.
Technologies: Deep Learning, Bokeh, Sentiment Analysis, Amazon Web Services (AWS), Neural Networks, Machine Learning, Data Science, Natural Language Toolkit (NLTK), React, Jupyter Notebook, MongoDB, Pandas, TensorFlow, Scikit-learn, Python, Artificial Intelligence (AI), Natural Language Processing (NLP), Generative Pre-trained Transformers (GPT), Finance, Hedge Funds, Scraping, PDF Scraping, Web Scraping, Selenium, Data Modeling, Forecasting, Data Analysis, Research, Decision Trees
Freelance Machine Learning Engineer
2016 - 2017
Wedifique
- Implemented a collaborative filtering learning algorithm using Python libraries for use in a product recommendation system.
- Enabled a learning algorithm to be influenced by administrator suggestions when deciding feature weighings.
- Updated aspects of the main web app, where necessary, on both the Node.js back end and AngularJS front end.
- Queried (using MongoDB) and derived data for use in user/trend analysis and to populate reports/graphs.
- Set up multiple-server web/worker infrastructures using AMQP with Heroku.
Technologies: Heroku, RabbitMQ, AMQP, Machine Learning, AngularJS, Node.js, MongoDB, NumPy, Pandas, Scikit-learn, Python, Artificial Intelligence (AI), Full-stack, Web Development, Recommendation Systems
Freelance Full-stack Developer
2016 - 2016
Swtch
- Created a geolocation web app's proof of concept using primarily AngularJS.
- Extensively used a Google Maps JavaScript API in an asynchronous manner, to create a map canvas, plot markers, as well as geocode them from addresses.
- Developed a complex user registration and booking system that was persisted to a PostgreSQL database using a pg-promise library in Node.js.
- Styled an app using Bootstrap so that it is responsive and can be used on a variety of devices.
- Implemented a RESTful API using Express.js and Node.js.
Technologies: Sass, CSS, HTML5, HTML, Gulp, PostgreSQL, Express.js, Bootstrap, Node.js, AngularJS, Full-stack, Databases, Web Development, Google Maps
Associate Developer
2013 - 2016
Goldman Sachs
- Collaborated with a global market risk business to design and maintain a platform that produced a bank’s risk metrics.
- Used the firm's Python-like proprietary language to build and test a framework to collate big data sets and to automate the creation of stress test reports for regulators.
- Led the development team that produced an AngularJS web app and RESTful API to allow users to adjust risk measures and audit these changes.
- Conducted interviews of lateral hires and of interns/analysts for the tech division.
- Assisted in integrating a platform into a new distributed computing framework, including occasional examination of the platform's core C++ code.
- Co-created a Java-based version system for report configurations that could be controlled via an AngularJS web app.
- Investigated machine learning methods for possible use in the department.
Technologies: SQL, Sybase, D3.js, Gulp, jQuery, DataTables, Sass, Java, Python, C++, Node.js, JavaScript, AngularJS, APIs, Full-stack, Databases, Finance, Web Development, Team Leadership
Analytics Developer
2010 - 2013
RBS Markets & International Banking
- Developed and maintained a .NET web-based application for analyzing and visualizing time series data for a range of financial products.
- Performed extensive regression testing and other analysis as part of the regular upgrades in pricing libraries the tool depended on.
- Implemented Agile methodologies in delivering several C# coding assignments to add new analytics.
- Trained the support and development teams around the globe including a trip to Singapore to facilitate this.
- Created a VBA tool for logging emails sent to the support inbox, which then detected whether they had been responded to. This was then summarized in a management report.
Technologies: .NET, SQL, Visual Basic for Applications (VBA), Oracle, ASP.NET, Microsoft SQL Server, C#, Databases, Finance, Data Visualization
Inside Licensing Specialist
2010 - 2010
Microsoft
- Automated the building of a spreadsheet which was compiled from various sources and also kept track of deal progress for a Munich-based licensing team.
- Created a tool using VBA to identify discrepancies between two customer pricing sheets (taking into account that entries may be present in both, but in different row locations).
- Presented these tools at team calls and wrote up documentation for them in English and German.
- Added new statistics for the account planning sheet including the data mining of past discounts given to customers.
- Assisted the licensing sales specialists with price and product migration queries.
Technologies: Microsoft Excel, Visual Basic for Applications (VBA)
Experience
Chord Extractor
https://github.com/ohollo/chord-extractorAn open-source Python library (with over 200 GitHub stars) for extracting chord sequences from sound files of multiple formats, with the option of leveraging multiprocessing to quickly source data from many files. It's primarily intended for those analyzing musical pieces and their harmonic progressions.
Harmonic and Chord Progression Analysis
https://huggingface.co/spaces/ohollo/harmonic-analysisA tool that examines the chord progression of a song and gives feedback on its originality, along with other songs whose chords/harmony are similar to those of the song. It achieves this by calculating vector similarities using embeddings in an index built with a custom Transformer model. That model is trained on my own dataset created using my chord-extractor tool (see above).
It also has an MCP server set up so an AI agent can call the tool. Some experimental agents and evals are set up to analyze chords and determine which songs are notable.
It also has an MCP server set up so an AI agent can call the tool. Some experimental agents and evals are set up to analyze chords and determine which songs are notable.
Oxford10
https://www.oxford10.comI worked as the president of the official young alum community of Oxford University in the London region. My duties included formulating and implementing social media strategy and website maintenance and heading a committee.
In my time on the committee, I analyzed the data from Eventbrite and Mailchimp to build a picture of our attendees and used it to inform our event strategy. I also maintained and promoted their website and social media profiles. Attendance grew stronger as a result.
In my time on the committee, I analyzed the data from Eventbrite and Mailchimp to build a picture of our attendees and used it to inform our event strategy. I also maintained and promoted their website and social media profiles. Attendance grew stronger as a result.
Adapting AI-generated Texts
Researched and recommended methodologies for startup generating and adapting texts to make them less detectable as being generated by AI, e.g., by ChatGPT.
These included NLP techniques achievable in the short term, such as examining parts of speech, dependency parsing, and manipulating grammar.
These included NLP techniques achievable in the short term, such as examining parts of speech, dependency parsing, and manipulating grammar.
Education
2008 - 2009
Master of Science Degree in Mathematics and the Foundations of Computer Science
University of Oxford - Oxford, England
2003 - 2007
Bachelor of Science Degree in Mathematics with a Study in Continental Europe
University of Bristol - Bristol, England
Certifications
JANUARY 2019 - PRESENT
Deep Learning
Deeplearning.ai
FEBRUARY 2016 - PRESENT
Machine Learning
Stanford University | via Coursera
NOVEMBER 2013 - PRESENT
Financial Engineering in C++
City, University of London (London, UK)
Skills
Libraries/APIs
Pandas, SpaCy, Natural Language Toolkit (NLTK), Google Chart API, TensorFlow, RxJS, Scikit-learn, React, D3.js, REST APIs, Google Maps, Node.js, jQuery, NumPy, SQLAlchemy, Flask-RESTful, Matplotlib, Keras, XGBoost, OpenCV, AMQP, Asyncio, PyTorch, Gradio
Tools
Gulp, AngularFire, PyCharm, Git, Mongoose, NPM, Sublime Text, Jira, Microsoft Visual Studio, TortoiseSVN, Amazon SageMaker, Jupyter, Microsoft Excel, RabbitMQ, Celery, DataTables, Browserify, IntelliJ IDEA, CVS, MATLAB, Karma, Gensim, Plotly, IBM Watson, Mailchimp, BigQuery, Claude Agent SDK
Languages
HTML, SQL, Python, HTML5, TypeScript, CSS, C++, JavaScript, C#, Visual Basic for Applications (VBA), Sass, Python 3, R, CoffeeScript, JADE, Java
Frameworks
Angular, Alembic, .NET, Flask, AngularJS, Angular Material, Bootstrap, Express.js, Jasmine, Ray, Django, ASP.NET, NUnit, Selenium, LangGraph
Paradigms
Object-oriented Programming (OOP), DevOps, ETL, RESTful Development, Asynchronous Programming, Agile Software Development, Test-driven Development (TDD), Model Context Protocol (MCP), Search Engine Optimization (SEO)
Storage
Google Cloud, MongoDB, PostgreSQL, Microsoft SQL Server, Databases, MySQL, Sybase
Platforms
Google Cloud Platform (GCP), Docker, Kubernetes, Jupyter Notebook, Amazon Web Services (AWS), Heroku, Firebase, Windows, Linux, Oracle, Visual Studio Code (VS Code), Twilio, WordPress, NVIDIA CUDA, Vertex AI, LangSmith
Industry Expertise
Social Media, Project Management
Other
Software Development, Marshmallow, APIs, Deep Learning, Artificial Intelligence (AI), Natural Language Processing (NLP), Data Science, Machine Learning, Mathematics, Data Analysis, Data Engineering, Freelancing, Data Modeling, Architecture, Full-stack, Generative Pre-trained Transformers (GPT), Large Language Models (LLMs), Research, Sentiment Analysis, Queue Management, Cryptography, Angular Bootstrap, Data Visualization, Statistics, Data, PDF, Computer Vision, Technical Leadership, Software Architecture, Finance, Hedge Funds, Forecasting, Machine Learning Operations (MLOps), Hugging Face, Deep Neural Networks (DNNs), Language Models, CTO, System Architecture, Containers, Neural Networks, Audio Processing, Graph Theory, Bokeh, Google Material Design, Librosa, Multiprocessing, MLflow, Image Processing, Dash, Leadership, Algorithms, Algebra, Number Theory, Elliptic Curve Cryptography, Quantum Computing, Calculus, Complexity Theory, Speech Recognition, Voice Analysis, Early-stage Startups, OpenAI GPT-3 API, Chatbots, Convolutional Neural Networks (CNNs), GloVe, Solution Architecture, AI Programming, Web Development, Eventbrite, Scraping, PDF Scraping, Web Scraping, LangChain, Data Augmentation, FAISS, Agentic AI, Recommendation Systems, Decision Trees, RAG Systems, Knowledge Graphs, LLM Reasoning, AI Project Management, Team Leadership, System Design, Multi-agent Systems
Collaboration That Works
How to Work with Toptal
Toptal matches you directly with global industry experts from our network in hours—not weeks or months.
1
Share your needs
Discuss your requirements and refine your scope in a call with a Toptal domain expert.
2
Choose your talent
Get a short list of expertly matched talent within 24 hours to review, interview, and choose from.
3
Start your risk-free talent trial
Work with your chosen talent on a trial basis for up to two weeks. Pay only if you decide to hire them.
Top talent is in high demand.
Start hiring