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Founded in 2014, FNA’s mission is to make the financial system safer and more efficient.


About the role

  • You will be actively involved in the research and development of exciting new tools and use-cases
  • Publish! We have interns publishing papers stemming from their projects at FNA
  • Using the FNA platform and scripting language, you will be assisting to identify hidden behavioural patterns and interconnections in large datasets, helping to create breakthrough solutions, performing exploratory and targeted data analyses as part of quantitative services engagements or proof of concepts
  • Partner with cross-functional teams to solve real-world business problems at scale and identify trends/opportunities for the customers
  • You will own projects from end to end, stamp your name to important work and document use cases in technical reports, white papers, etc.


Why join FNA? 

  • Rapidly advance your thinking, skills and career in a high growth tech company
  • Work remotely with an international team of innovative and agile colleagues
  • £1k/yr for training and continuing education
  • Unlimited holiday lets you take a break when you need it
  • International team off-sites in sunny locations 2x/yr (when travel permits)


Who we’re looking for 

  • You should be enrolled as a PhD student focused in Graph Theory, Finance/Econometrics, Data Science, Machine Learning, Statistics, Mathematics, or related applied quantitative field
  • Passion and curiosity for what is happening within RegTech/FinTech/SupTech, Big Data, Graph Databases, Data Analysis and especially Graph Analytics
  • Experience with MatLab, Python, R or similar
  • Experienced with, or have a desire to learn Network Science and/or Graph Analytics
  • Hands-on and curious; always looking to learn, solve problems and improve your skills
  • Strong written documentation skills to assist with articles/use-cases/releases etc.
  • Excellent written and spoken English


About FNA

Used by the world’s largest central banks and financial institutions, our technology enables them to uncover hidden connections in large complex datasets, visualize them via interactive dashboards and use artificial intelligence to predict and reduce the impact of anomalies and stress events within their networks.

Demand for our technology and experience has grown considerably as organizations realize the interconnected nature of their systems. As a result, we’ve spent the last 12 months expanding our team significantly to serve our customer base and boost our brand.



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