Administration

Senior Data Scientist (Remote)

Preferable Location(s): London, United Kingdom of Great Britain and Northern Ireland
Work Type: Full Time

About Us


We’re an early stage fintech founded by a team with deep financial services experience and with backing from sone of the world’s leading Venture Capital investors. We’re building a platform to help payments companies manage credit risk better. We are looking to hire a Senior Data Scientist to join our team and help us on our journey.


We believe diverse teams win and welcome applications from people of all backgrounds. Kinetic has built a collaborative, flexible and supportive culture where everyone can thrive. 

The Role

We’re looking for someone who is excited at the prospect of facing new, and really tricky, data challenges. Someone who is comfortable working as part of a team who are building things from scratch. We’re looking for someone with deep technical skills, but who is really motivated by making an impact on our business and our customers. 

Responsibilities:

  • Build, implement and monitor predictive credit risk models which help us accurately quantify the level of credit risk of businesses across multiple different geographies,
  • Liaise with various third party data providers (e.g. credit reference agencies and merchant acquirers) to define the data you need to build effective models, 
  • Liaise with various payments firms to understand their needs and help shape the solutions we bring to market,
  • Help guide, coach and develop the colleagues around you in data science tools and techniques, how to get the best out of your models, and how they can support the data science function (e.g. by helping define the data environment we need to develop),

We are looking for:

  • 2-5+ years experience building statistical models in Python and extracting/manipulating data in SQL is essential (ideally but not essentially including using a query engine like amazon athena or spark using pyspark or equivalent),
  • Credit risk modelling experience including building probability of default models is required (doing so for SME/commercial lending preferred but not essential),
  • Commercial acumen and an understanding of the economics underpinning credit portfolios would be beneficial,
  • Able to work autonomously and to display strong judgement and proactivity is essential,
  • Data orchestration experience using aws glue or airflow, data registry using glue catalog, visualization using quicksight or apache super set  would be beneficial (but not essential).
  • Some exposure to MLOps concepts like Model monitoring, Model drift, Data drift and/or some exposure to using MLOps tools like kubeflow are also nice to have. 

What you'll get in return:

  • Flexible, remote-first working arrangements
  • Opportunities to grow and progress and to work on problems no-one has worked on before
  • Competitive package including company equity

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