Description

You will build and deploy production-grade machine learning, NLP, and operations research solutions to optimize risk analytics and claims processing.

This role is remote.

Responsibilities

• Design, develop, and optimize predictive models for incident prioritization, claim severity, and financial exposure.
• Build NLP and LLM pipelines to extract insights from unstructured claims and incident narratives.
• Apply operations research techniques to optimize business decisions and resource allocation.
• Implement explainable AI frameworks and ensure model governance, drift monitoring, and retraining.
• Collaborate with Risk, Legal, and Data Engineering teams to translate business requirements into scalable technical solutions.

Required Skills

• 5+ years of experience in Data Science, Machine Learning, or related technical fields.
• Strong proficiency in Python, SQL, and Spark for large-scale data analysis.
• Hands-on experience with ML frameworks such as Scikit-Learn, XGBoost, TensorFlow, or PyTorch.
• Proven track record of deploying production models using MLOps practices and CI/CD.
• Experience with cloud platforms (AWS, Azure, or GCP) and data warehouses like Snowflake or Databricks.
• Deep understanding of NLP, LLMs, and feature engineering across structured and unstructured data.
• Ability to communicate complex analytical findings to executive stakeholders.

Preferred Skills

• Master’s or PhD in Computer Science, Statistics, Industrial Engineering, or Operations Research.
• Experience in risk analytics, claims processing, or incident management domains.

Key Skills

Education

Bachelor’s degree