Description

· 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

Core Responsibilities

Machine Learning & Predictive Modeling

· 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

Natural Language Processing (NLP) & AI

· 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

Data Science & Advanced Analytics

· 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

Cross-Functional Collaboration

· 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

Documentation & Governance

· Create documentation covering methodology, assumptions, validation approaches, and limitations

· Support model governance and explainability requirements

· Ensure compliance with data governance, privacy, and security standards

Machine Learning Frameworks

· Scikit-Learn

· XGBoost

· TensorFlow

· PyTorch

· MXNet

· LLM Frameworks

Cloud Platforms

· AWS

· Azure

· GCP

DevOps & MLOps

· CI/CD

· MLOps Frameworks

· Model Deployment & Monitoring

Education

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

Ideal Candidate Profile

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.

Key Skills

Education

Bachelor’s degree