Ollie Glass

Data product development

Contract data-scientist / developer working end to end from idea to implementation, building data products for sales, marketing, decision support and operations.

Feel free to connect or email me, ollie@ollieglass.com

Areas I work in

Data science - using statistics and machine learning to interpret data, extract insights and build predictive models.

Product build - full stack product development with Rails, JavaScript and Python.

Data strategy - finding ways to value and use your data for strategic advantages in sales, marketing, decision making and ops.

Communication - presenting insights to your investors, board, team and customers through slides, visualisations and interactives.

Architecture - data science research and data engineering planning, delivering you a roadmap and technical spec.

Workshops - short, fast, collaborative sessions to explore and evaluate ideas.

Work and clients


Asked to improve search results, I designed and built a machine learning system to rate image quality and factor it into search rankings, giving Picfair a market-leading image search engine.

The machine learning system is deployed into production as a scalable microservice, so as Picfair grows they can just add more servers to handle incoming images. It's seamlessly integrated into the existing tech stack, with testing, server monitoring, a clean API and integration into the Rails and Elasticsearch codebase.


I designed and built an interactive visualisation of London's property market, a system to find stories from rental trends each month, and data-driven marketing pages for Rentify's marketing team.

All of these tools are powered by a central data product. Combining Rentify's exclusive lettings data with 3rd party sources including London Datastore and Land Registry records, it creates a rolling, proprietary analysis of the London property and rental market.

Other recent work

  • Social Chain Data strategy and product management
  • QSA Data-driven marketing and visualisation
  • Silicon Milkroundabout Decision support tools for candidate vetting and scoring
  • Hire My Friend Marketplace analysis and automatic user segmentation
  • YouGov User behaviour analysis and data product roadmap
  • PeerIndex Personalised marketing automation and data product design

There's more details about my clients and projects on my LinkedIn profile.

Earlier work



I use Python for data science, making extensive use of the Jupyter Notebook with pandas, scikit-learn and specialised libraries for exploratory work and model building. I'm also happy using spreadsheets to share work with non data-scientists.

I've used Postgres and MySQL extensively, also NoSQL databases including MongoDB, Elasticsearch, Redis and Neo4j. I've worked with data from APIs, web scrapers, Excel spreadsheets, camera phones, sensors, countless csv files... I once wrote a parser and named entity recognition tool to import data from a team's emails.

For visualisations I like d3.js and plot.ly on the web, and use matplotlib and DOT for more technical charts and network diagrams.


I was a full stack web app developer before specialising in data science. I'm experienced with Rails, Postgres, Javascript, and even CSS (but I lean on Bootstrap as much as I can), also queues, caching, continuous integration etc. Using Python I've shipped apps with Django, SQLAlchemy and Flask.

For data science, Flask is great for putting web facing API wrappers around code, and I sometimes build quick apps for data labelling, ops or dashboard tools with Rails.

Side projects

When I'm not working I like to make things on the internet.

More about me

I discuss my approach to data science and product development in the Balderton podcast with George Spencer (Rentify) and Ben Goldsmith (Balderton).