Senior Machine Learning Engineer & Data Analyst – Financial Risk Scoring

• Experience with real-time scoring systems.
• Experience with distributed compute frameworks (Spark, Beam, Flink, etc.).
• Familiarity with regulatory or compliance-sensitive environments.
• Experience with graph-based risk models or transaction network analysis.
• Experience building internal analytics tools or dashboards.
• Knowledge of feature stores and model versioning systems.

• You think critically about data quality and signal reliability.
• You design models that are robust, explainable, and production-safe.
• You’re comfortable moving between analysis, modeling, and infrastructure.
• You can handle messy, real-world financial data at scale.
• You communicate clearly with engineers, product teams, and stakeholders.
• You care about correctness and long-term maintainability.

• Extending risk scoring models with new behavioral signals.
• Detecting anomalous transaction patterns across massive datasets.
• Improving precision/recall tradeoffs in production scoring.
• Building pipelines that process and index large transaction datasets.
• Designing model monitoring to detect drift and degradation.
• Optimizing large-scale Elasticsearch queries and aggregations.
• Combining rule-based and ML-based scoring systems.

• Competitive salary + performance incentives
• Equity aligned with long-term growth
• High ownership and direct exposure to leadership
• Remote-first with global team
• Health and sports benefits
• Yearly international team off-sites
• Work on high-impact financial risk systems.
• Tackle real-world ML challenges at large scale.
• Influence the architecture of our scoring and analytics platform.
• Collaborate with experienced engineers and data specialists.
• Own meaningful parts of our data and ML strategy.

Send us:

• A short introduction and relevant experience.
• Examples of ML or risk models you’ve worked on.
• Links to projects, papers, or code (if available).
• We’re particularly interested in candidates who can demonstrate experience building robust financial risk models on very large datasets and bringing them successfully into production.