Data Scientist – Fraud

• Develop best in-class ML models to predict claims outcomes, fraud and other risk KPIs
• Work with ML Engineers to deploy ML models
• Monitor deployed ML models and link performance to commercial value KPIs
• Present your work to Operational and Commercial stakeholders
• Explore new technologies and contribute to setting team’s best practices.

Requirements

• Proficiency in Python and experience with ML libraries for building and deploying machine learning solutions in cloud environments
• Ability to think commercially and link ML model performance to commercial KPIs
• Experience using structured query languages (SQL)
• Keen interest in emerging new ML techniques and their commercial value
• Ability to communicate across teams of Data Scientists, Data Engineers, ML Engineers, Operational and Commercial stakeholders.

Core Competencies

Demonstrates expertise in developing and deploying machine learning models, with a strong focus on linking model performance to commercial KPIs. Proficient in Python and SQL, with a keen interest in emerging ML techniques and best practices.

Highest-signal resume keywords

• Python Proficiency
• Machine Learning Model Development
• SQL Experience
• Commercial KPI Linking
• ML Model Deployment

Hard Skills

• Machine Learning
• Model Monitoring
• Data Analysis
• Statistical Modeling
• Predictive Analytics

Soft Skills

• Communication
• Collaboration
• Commercial Thinking

Industry Keywords

• Claims Outcomes
• Fraud Detection
• Risk KPIs
• Best Practices

Tools & Technologies

• ML Libraries
• Cloud Environments

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