Machine Learning Engineer – Risk Scoring, Model Monitoring

Chiri

. Develop and evaluate predictive models using XGBoost, LightGBM, and CatBoost for risk scoring, classification, and related decision support .

Posted 11/6/2026full-timeRemoteMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in developing and evaluating predictive models using advanced machine learning frameworks such as XGBoost, LightGBM, and CatBoost, with a strong focus on model performance, data integrity, and explainability in healthcare and finance contexts.

Highest-signal resume keywords

Production Machine Learning ExperienceXGBoost, LightGBM, CatBoost ExpertiseModel Evaluation and CalibrationData Leakage PreventionHealthcare and Pharmacy-Finance Experience

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Predictive ModelingFeature EngineeringModel EvaluationStatistical AnalysisAutomated MonitoringData Drift ManagementModel DeploymentReproducible Model PipelinesTemporal ValidationRisk Scoring

Soft Skills

Communication to StakeholdersProblem-Solving

Industry Keywords

Healthcare AnalyticsPharmacy-FinanceCredit ScoringClaims AnalyticsDeterministic RulesProbabilistic Models

Tech Stack

Tools & technologies

Python

About the role

Key responsibilities & impact

Develop and evaluate predictive models using XGBoost, LightGBM, and CatBoost for risk scoring, classification, and related decision support
Compare machine learning performance against deterministic rules and historical benchmarks
Detect changes in input distributions, prediction behavior, and model accuracy, with alerts when performance falls below defined thresholds
Build feedback loops using newly available outcome data, with retraining and validation procedures
Identify and prevent target leakage, temporal leakage, proxy leakage, and unintended bias in model features
Implement reproducible model pipelines, monitoring, performance reporting, explainability, and controlled model deployment
Determine where machine learning adds measurable predictive value to a healthcare and pharmacy-finance platform
Explain model performance, limitations, and business implications to technical and nontechnical stakeholders

Requirements

What you’ll need

Strong production machine learning experience using Python
Hands-on expertise with XGBoost, LightGBM, CatBoost, or comparable gradient-boosted tree frameworks
Deep understanding of statistics, feature engineering, model evaluation, calibration, and temporal validation
Experience detecting and managing data drift, concept drift, model degradation, and retraining triggers
Proven experience preventing data leakage and identifying proxy variables that compromise model validity
Experience developing production ML pipelines with automated monitoring, testing, and reproducibility
Ability to explain model performance, limitations, and business implications to technical and nontechnical stakeholders
Experience with healthcare, pharmacy, financial risk, credit scoring, or claims analytics
Experience with delayed outcome labels and historical transaction datasets
Experience deploying interpretable models in regulated environments
Experience combining deterministic rules with probabilistic models

Benefits

Comp & perks

The opportunity to help establish a rigorous, measurable approach to machine learning in a high-stakes business environment.