Sr. Data Scientist - Risk

The Sr Data Scientist, Risk will offer a strategic perspective, deep analytical and modeling capabilities, and a collaborative working style. Key skills include strong analytical skills and deep understanding of machine learning algorithms.

Overview

Department

Data Science & Analytics

Job type

Full time

Compensation

$223,661 - $268,333 per year

Location

San Jose, United States, North America

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Requirements

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

Responsibilities

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

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