Kraken

London

1,001-5,000 employees

We put the power in your hands to buy, sell, and trade digital currency

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Data Scientist - Compliance

Kraken

Data Scientist - Compliance

Data Science

This job is no longer open

About Kraken

As one of the largest and most trusted digital asset platforms globally, we are empowering people to experience the life-changing potential of crypto. Trusted by over 8 million consumer and pro traders, institutions, and authorities worldwide - our unique combination of products, services, and global expertise is helping tip the scales towards mass crypto adoption. But we’re only just getting started. We want to be pioneers in crypto and add value to the everyday lives of billions. Now is not the time to sit on the sidelines. Join us to bring crypto to the world.

About the Role

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.

This role will contribute directly to Kraken’s ability to build best-in-class systems for detecting financial crime and will enable us to continue to meet regulators’ expectations for a robust monitoring and control program.

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, R, 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.

Nice to have

Familiarity with cloud native and distributed processing would be a strong plus (for example Kubernetes)
Familiarity with financial crime and market abuse typologies is a plus.

Location Tagging: #EU #US

We’re powered by people from around the world with their own unique and diverse experiences. We value all Krakenites and their talents, contributions, and perspectives, regardless of their background.

As an equal opportunity employer we don’t tolerate discrimination or harassment of any kind. Whether that’s based on race, ethnicity, age, gender identity, citizenship, religion, sexual orientation, disability, pregnancy, veteran status or any other protected characteristic as outlined by federal, state or local laws.