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.
We all want to write cleaner code but usually don't know where to start. It also doesn't help that most guides available are written for software engineers and not data scientists.
Code smells are a taxonomy and a well-defined set of instructions on how to identify typical antipatterns in your code and change them in a few steps.
In this talk, I will select a short list of typical code smells that frequently appear in data-intensive workflows and walk you through how to resolve them.
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.