Data Science beyond packages and libraries

Recap Summary

We were delighted to be back in the MBN office in Glasgow for our most recent Data Science Community event, delivered in partnership with The Data Lab and Optima Partners.

We were joined by members of the Optima Partners Data Science Practice to traverse the myriad ways in which data-augmented decisioning is supported outside of a universe of packages and libraries routinely used by data scientists.

We explored the boundaries of existing packages, discussed ways to operate beyond-code and motivate the development of innovative algorithms to meet outstanding needs in industry.

3 Main Takeaways:

  1. - Software engineering principles within data science ML deployment - the benefits of unit testing and version control
  3. - The governance of Data Science - The different ways you can become a data scientist and how we can ensure it remains well governed
  5. - How ML can be used to leverage human genetic information to improve drug target identification and validation


Anyone who was not able to make it along to the event, don’t worry we have you covered.

You can watch the entire talk in full again here. Livestreamed by

Pictures from the event:

MBN Images for Blog (1)

Promo Video:

Guest Bio:

  • Dr Zhana Kuncheva is a Lead Data Scientist at Optima. Zhana holds a PhD in machine learning and high-dimensional modeling with research and industry collaborations centered on drug discovery - leveraging human genetic information to improve drug target identification and validation.​


  • Dr Manjula Dissanayake is a Lead Data Scientist at Optima. Manju holds a PhD in artificial intelligence, has extensive expertise and experience in banking and health, and significantly bridges the gap between data science and software engineering.


  • Scott Pirrie is a Lead Data Scientist at Optima. Scott has many years’ experience in advanced machine learning (ML) across insurance, financial services and telco. Scott led a pioneering project in the use of loyalty data for better lending in Asia and has designed and built numerous ML solutions deployed in blue-chip organisations. Scott is also currently studying part-time for a PhD in topological data science in health at the University of Edinburgh.