Welcome to the MBN Solutions Boss ‘n’ Data podcast show
You can watch our podcast series below or listen here:
In this episode I am joined by Dr Shorful Islam, co-founder and CEO of Be Data Solutions.
- In this Episode we discuss the following topics.
- End-to-end ownership of business-impacting projects
- Links models to KPIs and real outcomes
- Prioritising usable models over perfect ones
- Empathy for non-technical stakeholders
- Importance of collaborating effectively across functions
- Using critical thinking over tool-specific knowledge
- Being business-savvy, curious, and accountable
- Link to “Data Culture” https://www.koganpage.com/
authors/shorful-islam
Guest Bio:
With over 20 years of experience in the data and AI industry, Dr. Islam has been applying machine learning since his Psychology PhD—before “data science” became mainstream. Since then, he has helped global companies develop data strategies and build predictive models that drive business growth.
He specialises in building data functions across organisations such as ITV and Wunderman and is the author of Data Culture, a book on integrating data as a fundamental organisational component.
Beyond his leadership at Be Data Solutions, Dr. Islam founded Subatomic Analytics and advises multiple startups on data and AI. His career includes roles as Chief Data Scientist at Tribal Worldwide, Managing Director at Stream Intelligence, and Managing Partner at Wunderman, along with leadership positions at adam&eveDDB/DDB and ITV. He also brings experience from the NHS and local government.
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Meet the Host:
Joshua Smith, Associate Director – Client Acquisition, MBN Solutions
An industry veteran with over 13 years’ experience in talent acquisition for the Data Science and Analytics sector. Josh has deep knowledge of the domain experience – particularly around full data team builds and the intersection of analytics techniques with digital transformation. Josh’s experience has seen him partner with a high-profile corporate client to deliver successful search assignments within demanding parameters