The Data Dig

Nurturing a Data Science Team Within a Large Organisation (with Sanjeevan Bala)

Thu, 15 Jul 2021 00:08:34 +0100

Greg and Kelly chat to Sanjeevan Bala, Group Chief Data & AI Officer at ITV, and former Head of Data Science at Channel 4. Sanjeevan explains why decoding a company's culture is key to data success, how to set the right targets for a company's data function, and how to sell stakeholders on what data can do.

What is a Data Product Manager? (with Georgie Peake)

Fri, 02 Jul 2021 12:43:58 +0100

Kelly and Greg are back, and they have questions: What's a data product? What's a data product manager? Is their special guest one? Yes, she is! Georgie Peake, Data Product Manager at Filtered, talks you through those questions and more, breaking down what a data product manager does, why their skillset is important, and how their work can make businesses better.

Raising the Profile of Women in Data (with Katie Russell)

Thu, 10 Jun 2021 00:01:00 +0100

Kelly talks to Katie Russell, Data Director at OVO, to learn more about her career, and how companies can empower women to succeed in data science. Kelly and Katie discuss evidence-based approaches to inclusivity, finding mentors within your company, and succeeding as a female data leader.

Building a Diverse Data Science Team

Thu, 27 May 2021 00:01:00 +0100

Building a diverse team isn’t just moral — it makes good business sense, too. Evidence suggests that teams from different backgrounds, and with different opinions, punch above their weight. So how do you build (and retain) a diverse data science team? Kelly and Greg show you how to minimise bias in hiring, make employees feel secure when voicing different opinions, and foster a culture that helps your data team make the best decisions possible.

What is a Machine Learning Engineer?

Thu, 13 May 2021 01:15:21 +0100

'Machine Learning Engineer' is a pretty new job title, but demand for these mysterious maestros is stronger than ever. So what (if anything) makes them distinct from data scientists — and what sort of company would benefit most from hiring one? Join Greg and Kelly as they dig into what makes ML engineers unique.

How to Get a Job as a Data Scientist

Thu, 29 Apr 2021 00:01:00 +0100

So you want to get a job in data science — where do you start? Kelly and Greg guide you through every step, from finding the right job and polishing your CV to acing interviews and negotiating better terms. Tune in for the skinny on how to land your perfect role.

How to Hire a Data Scientist

Thu, 15 Apr 2021 00:01:00 +0100

So you want to hire a data scientist — where do you start? Kelly and Greg talk through the key steps: defining your data goals, writing a standout job description and securing the perfect hire. And there's more! Technical tasks, rocket science and how to avoid collecting underpants.

How to Build a Data Science Team (with Andreas Gertsch Grover)

Mon, 29 Mar 2021 18:29:32 +0100

Joined by guest Andreas Gertsch Grover (Director of Data at Scout24 SchweizAG, former Director of Data at Charlotte Tilbury), Greg and Kelly discuss what makes a great data science team: how to hire the right blend of people, where the data team should sit in the org chart, and how to empower a data team to be truly successful.

Data Science Jobs Explained

Mon, 29 Mar 2021 18:16:51 +0100

How do the roles in data science differ, and in what order should you bring them on? The 'AI Hierarchy of Needs' provides a useful guide. After meeting the most basic needs of purpose and data engineering, you can build reporting and business intelligence functions, before eventually adding data science and machine learning. Of course, that order may vary with organisation size and maturity, and there are a few extra factors that can also make a big difference.

What is Data Science?

Tue, 16 Mar 2021 17:53:23 +0000

Frankly, what even IS data science? In the inaugural episode, Greg and Kelly attempt to answer that question, explore how and when companies might start investing in data science, and introduce the main protagonists of the data science lifecycle. Disclaimer: this episode may inspire more questions than answers, and we don’t think that’s a bad thing.