Senior Machine Learning Engineer & Data Analyst – Financial Risk Scoring

Range is looking for a senior machine learning expert and data analyst to help design, extend, and operate financial risk scoring systems at scale. The role involves working on models and pipelines that process large datasets, improving scoring algorithms, and ensuring model reliability and interpretability in production environments.

Responsibilities:

• Design and improve financial risk scoring algorithms and models
• Analyze large-scale datasets (hundreds of TBs in Elasticsearch and related systems)
• Build and maintain data processing pipelines for feature generation, training, and evaluation
• Develop ML models for anomaly detection, fraud detection, credit/risk scoring, and behavioral analysis
• Validate models for accuracy, bias, stability, and drift over time
• Ensure models are explainable, auditable, and production-ready
• Work closely with engineering teams to deploy models into production systems
• 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

Requirements:

• Senior-level experience in machine learning and data analysis. (5+ years)
• Strong background in financial risk, fintech analytics, or fraud detection
• Experience building and deploying production ML models
• Strong Python ecosystem skills (NumPy, pandas, scikit-learn, PyTorch/TensorFlow, etc.)
• Experience with large-scale data processing (100s of TBs)
• Deep experience with Elasticsearch or similar distributed data stores
• Experience designing data pipelines (batch and/or streaming)
• 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 (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