Data Scientist - Compliance Systems

Remote

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Posted 2 weeks ago

About Kraken
Kraken is changing the world. Join the revolution!
Our mission is to accelerate the adoption of cryptocurrency so that you and the rest of the world can achieve financial freedom and inclusion. Founded in 2011 and with over 4 million clients, Kraken is one of the world’s largest, most successful bitcoin exchanges and we’re growing faster than ever. Our range of successful products are playing an important role in the mainstream adoption of crypto assets. We attract people who constantly push themselves to think differently and chart exciting new paths in a rapidly growing industry. Kraken is a diverse group of dreamers and doers who see value in being radically transparent.
In less than a decade Kraken has risen to become one of the best and most respected crypto exchanges in the world. We are changing the way the world thinks about money and finance. The crypto industry is experiencing unprecedented growth and Kraken is leading the charge. We’ve grown from 70 Krakenites in January 2017 to over 1200 today and we have no intention of slowing down.
The Compliance Systems team works at the intersection of Data Science, Product and Compliance. The Systems team is looking for a Data Scientist with a background in statistics, machine learning and/or natural language processing to help us build and maintain models to describe and predict anomalous customer activity on Kraken’s platform. The role will involve maintaining the transaction monitoring rules codebase, ensuring that requirements for model validation are met, and building a number of new models to enhance existing controls. There is significant scope for innovation in this role, and we are looking for someone who thrives in an environment of ‘freedom with responsibility’. The ideal candidate would have some familiarity with the control systems used to comply with regulatory requirements under BSA/AML or AMLD 4+, but this is not required. A great candidate will be someone who has experience solving customer and behaviour profiling problems in an evolving data environment.

Responsibilities

  • Partner with Compliance operations, Product, Engineering and other stakeholders to build descriptive and predictive models to identify anomalous activity.
  • Maintain the existing transaction monitoring rules codebase.
  • Ensure that all activities meet model validation requirements.
  • Design and build proof-of-concept models to test hypotheses and help with idea generation and refinement.
  • Use structured and unstructured data to enrich transaction monitoring metadata.
  • Drive cross functional projects from beginning to end: build relationships with partner teams.
  • Communicate model methodology and results to operations teams and Compliance leadership.
  • Develop anomaly detection and data modelling tools to monitor key performance indicators to improve the efficiency of models.

Requirements

  • PhD or Masters degree in Statistics, Computer Science, Physical Sciences, Economics, Math or a related technical field.
  • 3+ years industry experience in data science or analytics.
  • A consistent track record of performing data analysis using Python, SQL or other CLI tools.
  • Experience using statistics and predictive analytics to solve complex business problems.
  • Versatility and willingness to learn new technologies on the job.
  • The ability to clearly communicate complex results to technical and non-technical audiences.
  • Familiarity with big data tools such as Zeppelin, Spark, Kubernetes is a strong plus.
  • Familiarity with financial crime and market abuse typologies is a plus.


We’re powered by people from around the world with their own unique backgrounds and experiences. We value all Krakenites and their talents, contributions, and perspectives.
Check out all our open roles at https://www.kraken.com/careers. We’re excited to see what you’re made of.  
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Job tags: Big Data Crypto Economics Engineering Finance Kubernetes Machine Learning Python Spark SQL
Job region(s): Remote/Anywhere
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