Senior Machine Learning Engineer and Data Analyst Financial Risk Scoring

Skills

About the Role

You will design, extend, and operate financial risk scoring systems at scale. You will develop and validate machine learning models, build data pipelines, analyze large datasets, monitor model performance, and deploy reliable, explainable models into production.

Requirements

• Senior-level experience in machine learning and data analysis.
• 5+ years of experience.
• Strong background in financial risk, fintech analytics, or fraud detection.
• Experience building and deploying production machine learning models.
• Strong Python ecosystem skills, including NumPy, pandas, scikit-learn, PyTorch, and TensorFlow.
• Experience with large-scale data processing.
• Deep experience with Elasticsearch or similar distributed data stores.
• Experience designing batch and/or streaming data pipelines.
• Strong statistical reasoning and experimentation skills.
• Ability to translate business risk concepts into measurable model features.
• Experience evaluating model drift, bias, and long-term stability.
• Experience with real-time scoring systems.
• Experience with distributed compute frameworks such as Spark, Beam, or Flink.
• 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.

Responsibilities

• Design and improve financial risk scoring algorithms and models.
• Analyze large-scale datasets.
• Build and maintain data processing pipelines for feature generation, training, and evaluation.
• Develop machine learning models for anomaly detection, fraud detection, credit and risk scoring, and behavioral analysis.
• Validate models for accuracy, bias, stability, and drift over time.
• Ensure models are explainable, auditable, and production-ready.
• Deploy models into production systems with engineering teams.
• Optimize performance and cost across large-scale data infrastructure.
• Define metrics, dashboards, and monitoring for model performance.
• Investigate edge cases and failure modes in scoring systems.

Benefits

• Performance incentives
• Equity
• Remote-first work
• Health benefits
• Sports benefits
• Yearly international team off-sites