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· Predict which incidents are most likely to become claims
· Forecast claim severity and financial exposure
· Analyze unstructured incident and claims narratives using NLP and LLM technologies
· Develop explainable AI solutions that business stakeholders can trust
· Apply Operations Research techniques to optimize business decisions and resource allocation
· Deliver scalable production models that integrate into enterprise workflows
This role sits at the intersection of:
Risk Analytics + AI/ML + NLP + Operations Research + Business Optimization
· Design, develop, deploy, and optimize machine learning models
· Build incident prioritization and claim severity prediction models
· Develop risk-scoring frameworks for proactive risk identification
· Perform feature engineering across structured and unstructured datasets
· Monitor model performance, drift, retraining requirements, and scoring quality
· Develop NLP solutions for claims and incident narrative analysis
· Build text classification and language-processing pipelines
· Leverage Large Language Models (LLMs) to extract business insights
· Generate explainable AI outputs and risk-driver analysis
· Apply AI techniques to improve operational decision-making
· Develop predictive analytics and statistical modeling solutions
· Build record-linkage and entity-resolution models where unique identifiers do not exist
· Support large-scale data analysis across enterprise datasets
· Work with structured, semi-structured, and unstructured data sources
· Partner with Risk Management, Legal, Data Engineering, Data Governance, BI, and MLOps teams
· Translate business requirements into technical solutions
· Present findings and recommendations to technical and executive stakeholders
· Mentor junior Data Scientists and contribute to team best practices
· Create documentation covering methodology, assumptions, validation approaches, and limitations
· Support model governance and explainability requirements
· Ensure compliance with data governance, privacy, and security standards
· Scikit-Learn
· XGBoost
· TensorFlow
· PyTorch
· MXNet
· LLM Frameworks
· AWS
· Azure
· GCP
· CI/CD
· MLOps Frameworks
· Model Deployment & Monitoring
Required:
· Master's Degree in:
o Computer Science
o Statistics
o Industrial Engineering
o Operations Research
o Related Technical Field
Preferred:
· PhD in a relevant discipline
The strongest candidates will demonstrate:
· Deep Operations Research expertise
· Strong AI/ML engineering capabilities
· Hands-on NLP and LLM experience
· Experience building production machine learning systems
· Claims, risk, or incident analytics experience
· Ability to communicate complex analytical findings to business stakeholders
· Strong understanding of model explainability and governance
· Experience deploying scalable enterprise AI solutions
This role is best suited for a senior-level Data Scientist who can move beyond experimentation and deliver measurable business value through production-ready AI, NLP, and optimization solutions.
Bachelor’s degree
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