Senior Data Scientist

at  ComplyAdvantage

ABOUT US:

ComplyAdvantage is the financial industry’s leading source of AI-driven financial crime risk data and detection technology. Our mission is to neutralise the risk of money laundering, terrorist financing, corruption, and other financial crime.
More than 1000 companies rely on us to understand the risk of who they’re doing business with through the world’s only global, real-time database of people and companies. Our solutions identify thousands of risk events daily from millions of structured and unstructured data points.
We have five global hubs in New York, London, Singapore, Lisbon and Cluj-Napoca and are backed by Goldman Sachs, Ontario Teachers, Index Ventures, and Balderton Capital.
Since 2014, we have raised over $100 million in funding, and in 2022 alone grew by over 40% to over 500 people globally. Over the next 12 months, as our revenue increases, we plan to increase to 600.
At ComplyAdvantage diversity fuels our rocket ship and our commitment to inclusion across race, gender, age, religion, identity and experience drives us forward every day. We encourage everyone to apply and aspire to consider every application fairly.
We will handle your information in accordance with our Privacy Policy. For further information, please click here

Good data is at the heart of detecting financial crime and our Data Science team is key. As a Senior Data Scientist you are critical to powering ComplyAdvantage’s Financial Crime solutions and preventing financial crime globally.

As a Senior Data Scientist, you will:

• Work alongside our Engineering, Data Analytics and Product Teams to process huge amounts of structured and unstructured data to extract information.
• Lead and influence the development of innovative data products from conception to operationalisation.
• Collaborate with different teams to perform cross data source analysis for better data unification.
• Continuously research tools, technologies, frameworks and practices to ensure that we are ahead of the curve.
• Have a deep understanding of existing data sources, related challenges and potential improvements.
• Use exploratory data analysis and data mining techniques to identify actionable items for product improvement.
• Have ownership of data metrics, you will be designing data metrics for each machine learning module using business requirements and customer feedback.
• Provide data insights and visualisations which will act as insight for new ideas that will be part of our product roadmap.