Interview with – Leanne Fitzpatrick
summary
Explains concrete startup checks: assess data maturity (Financial Times maps subscriptions to customers, built a “breadth of reading” recommender to avoid echo chambers), a clear unique value proposition, and a strong team plus focus on data privacy and security. Argues “problem first, tech later” and using business KPIs (lifetime value, quality of subscribers) to judge projects. Calls out two surprises: ongoing maintenance and model observability are underinvested, and staff churn (~1–1.5 years) breaks continuity — practical, not theoretical, guidance.
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chapters (7)
- 0:00Start
- 0:24What makes Data Science in the media and news industry unique? And what advice would you give to someone looking to pursue a career in Data Science within this field?
- 6:20How can we use Data Science to empower ourselves into being in control of this process?
- 10:56How should we as data scietist can aproach bussineses to help them ease in this data science an AI that are not kinda comfortable and they haven't really had expiernce with? Should we - as technology experts - take more initiative in proposing what business can use - or should we be more of the ones who should listen to the problems of the people and then create solutions?
- 14:35Why is it in our industry that we are not putting more emfassis on maintenance of projects?
- 19:12What would be your message to every girl & young woman who would like to create a successful career in Data Science & AI? How much is the gender equality question important for our sector?
- 26:06Since this is your 3rd DSC - how do you think we have been progressing in the last couple of years and why should people continue to come to the DSC Events?
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