Jack Agass

I've always loved learning new things and tackling problems, which naturally led me to a career in data science and machine learning. I studied engineering at university, and my background there fuelled my passion for problem-solving and maths, making data science a perfect fit for me.

I am particularly fascinated by the broad spectrum of topics within data science, with a keen interest in building products from conception to production. The opportunity to apply this in the sports industry was a major appeal for me to join the Gemba team. My specific areas of interest include Bayesian statistics, recommendation systems, and the application of large language models.

Beyond my professional interests, I am a big sports fan, particularly following football and cricket. This passion for sports has been a constant in my life. Additionally, I enjoy listening to music, going to festivals, watching films, and all things related to food and wine. I try to squeeze in some exercise to stay fit and justify indulging in all the fun stuff.

6:00am – Alarm goes off. I get up and try to do some exercise, either going for a run or attending a gym class.

7:15am – Commute and find coffee, all while listening to a football podcast.

8:00am – Quick catch-up on emails and IM messages.

8:30am – Stand-up meeting. Review ongoing tasks and highlight any blockers or issues.

9:00am – Get down to work. Review outstanding tickets, check for PRs that need my attention and check the status of existing pipelines/workflows.

10:00am – Exploratory Data Analysis. Dive deep into datasets to uncover patterns, detect anomalies, and form hypotheses. This is a crucial step to ensure the quality and relevance of data before building any models.

11:30am – Familiarise myself with new client environments. Sometimes, working with new clients requires adapting to their environment as opposed to using our own cloud services.

12:00pm – Lunch.

1:00pm – Meetings. I try to schedule meetings in blocks, either in the morning or the afternoon. Following the Maker vs. Manager framework helps maximise my efficiency in order to minimise unnecessary context switching.

2:00pm – Model Development and Testing. Work on developing new machine learning models or improving existing ones. This includes experimenting with different algorithms, tuning hyperparameters, and evaluating model performance using various metrics. Collaborate with team members to ensure models align with business goals and deploy them to production environments when ready.

4:00pm – Research. A big part of working in data science and machine learning is keeping up to date with new technologies and trends. This involves reading research papers, watching talks or lectures, and exploring the latest developments in the open-source community. This research isn't limited to ML but also includes data engineering, DevOps, and software engineering.

5:30pm  –  End of the day. If I didn't exercise in the morning, I do it now.

7:00pm  – Home time.