Sr Data Scientist, Risk Strategy

About the role

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. The role requires a strategic mindset to shape the risk agenda and translate complex findings into actionable business impact. This position is critical in driving the integrity of the platform through advanced analytical methods. The successful hire will own the end-to-end analytical lifecycle from problem framing to productionization of risk models.

Key facts

What you'll do

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, enabling the discovery of hidden patterns and anomalies without relying solely on labeled data.

Champion the rigorous validation of models and experiments to ensure robustness, fairness, and performance before deployment to production environments.

Design and execute tests to measure the impact of risk controls and model changes, interpreting results to guide strategic decisions.

Synthesize complex analytical results into clear narratives and recommendations that influence product and policy decisions at scale.

Continuously explore emerging techniques and data sources to maintain a领先 edge in risk detection capabilities.

Translate ambiguous business problems into well-defined analytical plans, aligning with regulatory and operational constraints.

Own the development of monitoring frameworks to track model drift, data quality, and key risk indicators over time.

Partner with compliance and legal teams to ensure that methodologies meet industry standards and internal governance requirements.

Mentor junior analysts and data scientists by providing feedback on methodology, code quality, and analytical rigor.

Requirements

8+ years of experience in data science, risk, fraud, or related analytical roles.

Bachelor's degree in Computer Science, Statistics, Mathematics, Economics, or a related technical field; advanced degree preferred.

Expertise in SQL and at least one programming language, such as Python or R, for data analysis and modeling.

Proven experience building and deploying machine learning models in production environments.

Strong understanding of supervised and unsupervised learning algorithms, including regression, classification, clustering, and ensemble methods.

Experience with graph analytics, link analysis, or network-based methods is highly relevant for this role.

Demonstrated ability to communicate complex analytical concepts to both technical and non-technical stakeholders.

Experience working with large datasets and scalable data processing frameworks is essential.

A methodical approach to root cause analysis and troubleshooting complex data issues.

Nice to have

Experience in fintech, crypto, or digital payments risk environments.

Knowledge of blockchain data analysis and transaction monitoring.

Familiarity with regulatory frameworks such as AML/CFT and sanctions screening.

Experience with real-time streaming data and event-driven architectures.

Background in designing experiments such as A/B tests and uplift modeling.

Practical notes

Location: This role is based in San Jose, California, United States.

Employment type: Full-time.

Compensation: Base pay range $180,000.00 to $250,000.00.

No visa sponsorship is available for this role at this time.

Eligible candidates must be authorized to work in the United States without sponsorship.

Candidates must be able to commute to or be relocated to San Jose, California, United States.

The listed compensation range reflects target base pay only and does not include potential variable pay or equity awards.