Senior Data Scientist (Risk Strategy)

OKX; San Jose (CA); On-site; USD 140,000 - 210,000; Full time

Job summary

OKX is seeking a Senior Data Scientist in Risk to provide strategic analytics, model development, and deep investigative capabilities in a fast-paced fintech environment.

The role focuses on identifying sophisticated fraud patterns, building scalable models, and collaborating with product and engineering teams to enhance risk systems and data-driven decision making.

Qualifications

• Master's degree (or PhD) in Statistics, Mathematics, Operations Research, Computer Science, Economics, Engineering or other quantitative discipline.
• 4+ years of fraud analytics experience in financial services or FinTechs.
• Experience in a fast-paced startup environment with strong initiative.
• Crypto/Blockchain experience.

Responsibilities

• Identify complex fraud patterns and their technical root causes through data mining and analysis.
• Share new data mining techniques and maintain technical reference documentation.
• Collaborate with business and technology stakeholders to communicate findings to both technical and non-technical audiences.
• Provide technical guidance for engineering projects that integrate new data points into investigations.
• Link Analysis/Graph analytics to detect deeply-connected fraud networks and new account additions.
• Develop machine learning models and collaborate with product and engineering teams to implement features.

Job description

• The Senior Data Scientist, Risk will offer a strategic perspective, deep analytical and modeling capabilities, and a collaborative working style
• 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

The right candidate will have strong intellectual curiosity and passion for achieving business resultsAn ability to quickly define the problem, research and leverage state-of-the-art modeling techniques, and provide timely recommendations will be essentialKey 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 troubleshootingDemonstrated capacity for innovation and outside-the-box thinking in the creation of new capabilities and processes that are unstructured or exploratory in natureMaster'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 consideredSuccessful track record of owning and driving large, complex data analysis projectsCrypto/Blockchain experienceExperience in a fast-paced startup environment with a strong level of initiative; andAbility and willingness to travel as neededStrong communicator in both writing and speaking4+ years of fraud analytics experience in financial services or FinTechsDeep understanding of modern machine learning techniques / algorithms including GBM, XGBoost, LGBM, etc. Advanced programming skills of statistical / analytical software (SQL, R, Python,etc.)Multi-tasking and strong project management skillsHands-on experience/knowledge of modeling in machine learning (GBM, XGBoost, Random Forest, etc.); and