About the Role
As you take on this role, you will define the ML data and feature strategy for fraud detection, and own the end to end feature engineering pipelines. You will diagnose gaps between tooling and needed solutions, and drive the roadmap to close them. You will partner with Machine Learning Engineers to translate analytical insights into production ready ML systems, ensuring models are instrumented, monitored, and continuously improved. You will set the technical direction for ML Analytics, mentoring junior team members who need a senior practitioner to define the approach and translate direction into execution. You will partner cross functionally with Product Managers and Risk analysts to surface fraud signals and translate ML findings into business impacting decisions. You will serve as the team's institutional knowledge resource on ML industry evolution, helping the organization understand why certain solutions work, what historical architectural decisions mean for current tooling, and where the industry is headed next.
Requirements
- 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 this is the core stack. SQL and rule-writing are adjacent skills; they are not what this role is about.
- 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 over the past decade from Hadoop-era big data to modern feature stores like Tecton and the ability to apply that knowledge to close infrastructure gaps.
- A curated, high-precision approach to ML problems: you understand that in fraud and risk, you are optimizing for sensitivity and accuracy on a small fraction of high-stakes traffic not the broad-coverage, high-volume approach used in growth or ads.
- Background in risk or payments ML is strongly preferred candidates who have operated in this domain understand the problem framing intuitively.
- A passion for fighting fraud and abuse, and the curiosity to self-drive investigations, identify patterns, and find the root cause
- Demonstrates the ability to responsibly use generative AI tools and copilots (e.g., LibreChat, Gemini, Glean) in daily workflows, continuously learn as tools evolve, and apply human-in-the-loop practices to deliver business-ready outputs and drive measurable improvements in efficiency, cost, and quality.
- Experience with modern ML feature stores (Tecton, Feast, or equivalent).
- Prior work at FinTech companies, payments platforms, or risk solution vendors
- Familiarity with crypto-specific fraud vectors including ATO, scam flows, and onchain transaction patterns.
Responsibilities
- Define the ML data and feature strategy for fraud detection, determining what data needs to enter our systems so our models can take intelligent, high-accuracy action on a small fraction of traffic where intervention matters most.
- 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 leveraging your understanding of how the industry has evolved to make the right architectural calls.
- 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 who need a senior practitioner to define the approach and translate direction into execution.
- 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, what historical architectural decisions mean for current tooling, and where the industry is headed next.