Staff ML Risk Analytics
WFA Digital Insight
The demand for machine learning specialists in finance has surged 27% in the last year, driven by the need for sophisticated fraud detection. As a result, roles like this Staff ML Risk Analytics position at Coinbase are highly sought after. With the company's commitment to increasing economic freedom, this role offers a unique opportunity to apply technical expertise to real-world problems. Candidates should be prepared to demonstrate their proficiency in Spark, Python, and big data ML, as well as their ability to drive technical direction and mentor junior team members. Before applying, it's essential to understand the company's remote-first approach and the importance of collaboration in a distributed team.
Job Description
About the Role
The Staff ML Risk Analytics role at Coinbase is a critical position that sits at the intersection of fraud intelligence and machine learning infrastructure. As a key member of the Growth & Risk team, you will be responsible for defining the ML data and feature strategy for fraud detection, determining what data needs to enter the company's systems to enable intelligent, high-accuracy action. This role requires a deep understanding of the ML industry and its evolution, as well as the ability to apply that knowledge to complex, high-stakes fraud problems.The successful candidate will be a seasoned machine learning analytics professional with a strong background in risk, fraud, or payments problems. You will be working closely with cross-functional teams, including Product Managers and Risk analysts, to surface fraud signals and translate ML findings into business-impacting decisions.
Coinbase is a remote-first company, but not remote-only. As such, you can expect to participate in quarterly in-person working sessions, known as 'surges,' which are designed to foster collaboration and drive innovation.
What You Will Do
- Define the ML data and feature strategy for fraud detection, determining what data needs to enter the company's systems to enable intelligent, high-accuracy action
- Own the end-to-end feature engineering pipeline, identifying, building, validating, and promoting features that drive measurable improvements in ATO and scam ML performance
- Diagnose gaps between current tooling infrastructure and the solutions needed, and drive the roadmap to close them
- Partner with Machine Learning Engineers to translate analytical insights into production-ready ML systems, ensuring models are instrumented, monitored, and continuously improved
- Set technical direction for the ML Analytics function within Growth & Risk, mentoring junior team members and defining the approach
- Partner cross-functionally with Product Managers and Risk analysts to surface fraud signals and translate ML findings into business-impacting decisions
- Serve as the team's institutional knowledge resource on ML industry evolution, helping the organization understand why certain solutions work and where the industry is headed next
- Develop and maintain a deep understanding of the company's systems, tools, and technologies, and apply that knowledge to drive innovation and improvement
What We Are Looking For
- 8+ years of hands-on experience in machine learning analytics, data science, or a related technical field, with meaningful experience applied to risk, fraud, or payments problems
- Deep, practitioner-level expertise in Spark, Python, and big data ML
- Proven experience in feature engineering for ML models, including identifying the right signals, building pipelines, and validating feature quality at scale
- Holistic understanding of how the ML industry has evolved and the ability to apply that knowledge to complex, high-stakes fraud problems
- Strong technical leadership and mentoring skills, with the ability to define the approach and translate direction into execution
- Excellent communication and collaboration skills, with the ability to work effectively with cross-functional teams
- Strong analytical and problem-solving skills, with the ability to drive innovation and improvement
Nice to Have
- Experience working in a remote-first environment and participating in in-person working sessions
- Knowledge of SQL and rule-writing, although not essential for the role
- Experience working with large datasets and complex systems
- Familiarity with the financial industry and the challenges of fraud detection and prevention
Benefits and Perks
- Competitive salary and equity package
- Comprehensive health insurance and benefits package
- Generous PTO and flexible working hours
- Remote stipend and support for home office setup
- Opportunities for professional development and growth
- Access to cutting-edge technologies and tools
- Collaborative and dynamic work environment
- Quarterly in-person working sessions, known as 'surges,' which are designed to foster collaboration and drive innovation,
How to Stand Out
- Make sure to highlight your experience with Spark, Python, and big data ML in your application, as these are essential skills for the role.
- Be prepared to discuss your approach to feature engineering and how you have applied it in previous roles.
- Showcase your understanding of the ML industry and its evolution, and be prepared to explain how you stay up-to-date with the latest developments.
- Emphasize your ability to work effectively in a remote-first environment and participate in in-person working sessions.
- Be prepared to provide examples of your technical leadership and mentoring skills, and explain how you approach collaboration and communication with cross-functional teams.
- Consider creating a portfolio or samples of your work to demonstrate your expertise in machine learning analytics and data science.
This is a remote position listed on WFA Digital, the platform for professionals who work from anywhere. Browse more remote jobs across all categories.