- Blog
- CV generator
- Community
- Documentation
Brendan O'Brien
Melbourne, Australia
Much of consulting is about knowing how to ask the right questions. Only after the problem space is well defined can we engineer the right solution. I spent my early career as a scientist learning how to ask the right questions. My research focussed on understanding how the brain decomposes and processes the visual world so we can make sense of it. The data we uncovered helped us to design, build and test the effectiveness of the Bionic Eye. Nowadays, I use my experience as a scientist to help clients ask the right questions, my understanding of data analysis to create valuable insights and my skill as a software engineer to build the right solution.
CodersRank Score
What is this?
This represents your current experience. It calculates by analyzing your connected repositories. By measuring your skills by your code, we are creating the ranking, so you can know how good are you comparing to another developers and what you have to improve to be better
Information on how to increase score and ranking details you can find in this blog post.
527
CodersRank Rank
Top 2%
Top 1
ERB
Developer
Australia
Top 5
Ruby
Developer
Australia
Top 50
SCSS
Developer
Australia
Highest experience points: 0 points,
0 activities in the last year
Ruby
192.4
exp.
JavaScript
56.8
exp.
Python
14.7
exp.
Jupyter Notebook
11.6
exp.
List your work history, including any contracts or internships
DiUS
Apr 2021 - Feb 2022 (10 months)
Consultant
Engineering consultant doing various projects. Currently working in fintech building microfrontends and cloud uplift.
Envato
Sep 2018 - Mar 2021 (2 years 6 months)
Engineer
Internationalisation of Envato Elements
As part of the i18n team, I helped construct two Ruby on Rails apis using an event sourced approach. The Translation Bot API watched all github merges to master and extracted any new or changed frontend copy to the site and forwarded these to the Translation Service. The Translation Service API received requests and forwarded them onto external services for either human (frontend copy) or machine (item titles/descriptions) translations. Completed translations were made available for consumption on the event feed.
Tech Stack: Ruby/Rails, RSpec, Postgresql, Heroku, Buildkite, AWS Translate, Datadog, SumoLogic
Conversion Funnel Improvements.
Using Hypothesis Driven Design, we improved conversion on Envato Elements by over 40%. Our cross-functional Agile team worked together to propose hypotheses, design, engineer and analyse the results of user behavioural data acquired via Google Analytics/Optimize. Results were used to confirm or reject the experimental changes and used to inform the next round of improvements.
Tech Stack: ReactJS, Node, Cypress, Jest, Google Analytics & Optimize
As part of the i18n team, I helped construct two Ruby on Rails apis using an event sourced approach. The Translation Bot API watched all github merges to master and extracted any new or changed frontend copy to the site and forwarded these to the Translation Service. The Translation Service API received requests and forwarded them onto external services for either human (frontend copy) or machine (item titles/descriptions) translations. Completed translations were made available for consumption on the event feed.
Tech Stack: Ruby/Rails, RSpec, Postgresql, Heroku, Buildkite, AWS Translate, Datadog, SumoLogic
Conversion Funnel Improvements.
Using Hypothesis Driven Design, we improved conversion on Envato Elements by over 40%. Our cross-functional Agile team worked together to propose hypotheses, design, engineer and analyse the results of user behavioural data acquired via Google Analytics/Optimize. Results were used to confirm or reject the experimental changes and used to inform the next round of improvements.
Tech Stack: ReactJS, Node, Cypress, Jest, Google Analytics & Optimize
Factil
Dec 2017 - Aug 2018 (8 months)
Data Scientist/ Software Engineer
Interstate Child Protection Data Matching
After the Royal Commission into Child Abuse Factil was contracted by the Federal Government to produce a means for collaboration among state child protection systems whilst maintaining individual privacy. We used Machine Learning to identify matching records in child protection data across states combined with Feature Engineering to define the parameters. Case workers were then notified that additional interstate data was available.
Tech Stack: Python, Jupyter Notebooks, SciKit-learn, Seaborn, Ruby/Rails, Postgresql, SQL
After the Royal Commission into Child Abuse Factil was contracted by the Federal Government to produce a means for collaboration among state child protection systems whilst maintaining individual privacy. We used Machine Learning to identify matching records in child protection data across states combined with Feature Engineering to define the parameters. Case workers were then notified that additional interstate data was available.
Tech Stack: Python, Jupyter Notebooks, SciKit-learn, Seaborn, Ruby/Rails, Postgresql, SQL
Request failed with status code 503