Build and deploy ML models to detect fraud and analyze risk patterns in crypto trading, using graph analytics and unsupervised learning to uncover sophisticated threats.
At OKX, we believe that the future will be reshaped by crypto, and ultimately contribute to every individual's freedom.
OKX is a leading crypto exchange, and the developer of OKX Wallet, giving millions access to crypto trading and decentralized crypto applications (dApps). OKX is also a trusted brand by hundreds of large institutions seeking access to crypto markets. We are safe and reliable, backed by our Proof of Reserves.
Across our multiple offices globally, we are united by our core principles: We Before Me, Do the Right Thing, and Get Things Done. These shared values drive our culture, shape our processes, and foster a friendly, rewarding, and diverse environment for every OK-er.
About the Opportunity:
The Sr Data Scientist, Risk will offer a strategic perspective, deep analytical and modeling capabilities, and a collaborative working style. The right candidate will have strong intellectual curiosity and passion for achieving business results.
An ability to quickly define the problem, research and leverage state-of-the-art modeling techniques, and provide timely recommendations will be essential. Key skills will include a strong analytical mindset, deep understanding of most popular machine learning algorithms and lead key initiatives with integrity and a passion for investigations, problem solving, and troubleshooting.
What You’ll Be Doing:
Identify complex fraud patterns and their technical root causes through detailed data mining and analysis, including identification of sophisticated fraud methods employed by actors who are deliberately trying to avoid detection.
Serve as technical SME by sharing new data mining techniques, maintaining technical reference documentation, and interfacing with partner technology teams.
Collaborate across business and technology stakeholders to communicate analytical findings to both technical and non-technical audiences.
Provide technical guidance for engineering projects that incorporate new data points into the investigation team’s toolkit, such as API integrations or internal data transformations.
Link Analysis/Graph analytics to find and mitigate deeply-connected fraud networks and detect new accounts being added to these networks
Unsupervised learning methods to augment existing supervised models, or detect portfolio anomalies
Development of machine learning models
Partner with product and engineering team in implementing features and models, and enhancing systems
What We Look For In You:
Master’s degree (or PhD) in Statistics, Mathematics, Operations Research, Computer Science, Economics, Engineering or other quantitative discipline. Bachelor’s degree with significant relevant experience will be considered.
4+ years of fraud analytics experience in financial services or FinTechs
Crypto/Blockchain experience
Deep understanding of modern machine learning techniques / algorithms including GBM, XGBoost, LGBM, etc. Advanced programming skills of statistical / analytical software (SQL, R, Python,etc.);
Successful track record of owning and driving large, complex data analysis projects.
Demonstrated capacity for innovation and outside-the-box thinking in the creation of new capabilities and processes that are unstructured or exploratory in nature.
Experience in a fast-paced startup environment with a strong level of initiative; and
Ability and willingness to travel as needed.
Strong communicator in both writing and speaking
Multi-tasking and strong project management skills
Nice to Haves:
Hands-on experience/knowledge of modeling in machine learning (GBM, XGBoost, Random Forest, etc.); and
Perks & Benefits
Competitive total compensation package
L&D programs and Education subsidy for employees' growth and development
Various team building programs and company events
Wellness and meal allowances
Comprehensive healthcare schemes for employees and dependants
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