06-04, 15:45–16:25 (Europe/London), Beaumont
This discussion session focuses on exploring the application of software engineering practices in the field of data science. Join us to delve into essential aspects such as python packages, IDEs, testing, refactoring, and architecture that play a crucial role in building robust and scalable data science solutions. We will discuss how adopting software engineering principles can enhance the reliability, maintainability, and efficiency of data science projects. Whether you're a DS manager or practitioner, this session offers a platform to exchange insights, share experiences, and discover innovative approaches to integrating software engineering practices into the data science workflow.
This discussion session focuses on exploring the application of software engineering practices in the field of data science. Join us to delve into essential aspects such as python packages, IDEs, testing, refactoring, and architecture that play a crucial role in building robust and scalable data science solutions. We will discuss how adopting software engineering principles can enhance the reliability, maintainability, and efficiency of data science projects. Whether you're a DS manager or practitioner, this session offers a platform to exchange insights, share experiences, and discover innovative approaches to integrating software engineering practices into the data science workflow.
No previous knowledge expected
I run Hypergolic, a consultancy in London specialising in Machine Learning Product Management.
Formerly I was Head of Data Science at Arkera, a fintech startup in London, where I built market intelligence products with Natural Language Processing for Tier 1 investment banks and hedge funds.
Before that, I worked in mobile gaming for King Digital (makers of Candy Crush), specialising in player behaviour and monetisation.
I started my career as a quant researcher writing trading strategies at multiple investment managers.
I am currently a Machine Learning Engineer at Yanmar R&D Europe.
I previously transitioned from the world of aftersales to data science in early 2020 and got my first role as a data scientist at la Marzocco, where I built ETL pipelines on AWS and managed the cloud infrastructure with Terraform. A year and a half later, I joined Yanmar and have been working on applying machine learning in the engine and powertrain sector.
I value code quality and have a bit of a soft spot for software design.