Note: The job is a remote job and is open to candidates in USA. The Judge Group is seeking a Data Scientist to design and implement machine learning and natural language processing solutions to support risk and incident analytics. The role involves building predictive models and partnering with cross-functional teams to enhance data-driven decision-making and improve operational outcomes.

Responsibilities

• Translate ambiguous business problems into clearly defined data science solutions aligned with measurable KPIs
• Design, build, and deploy machine learning models for incident prioritization, claim prediction, and severity classification
• Develop feature engineering pipelines using structured and unstructured data sources
• Apply NLP techniques to extract insights from incident and claim narratives
• Implement record linkage methods to connect related datasets without clean unique identifiers
• Build and validate predictive models to assess risk likelihood and financial impact
• Generate explainable outputs, including key drivers and interpretable insights for business users
• Partner with cross-functional teams including Risk, Legal, Engineering, Analytics, and Governance to deliver scalable solutions
• Monitor model performance, data drift, and system reliability; iterate and retrain models as needed
• Document methodologies, assumptions, and validation results to ensure reproducibility and transparency
• Ensure compliance with data governance standards, including proper handling of sensitive data
• Communicate insights and recommendations to technical and non-technical stakeholders
• Mentor junior team members and contribute to a culture of continuous learning and innovation

Skills

• Master's degree in Computer Science, Statistics, Industrial Engineering, or a related technical field, or equivalent practical experience
• 5+ years of experience in data science, machine learning, or operations research (or 2+ years with a PhD)
• Experience building and deploying ML models using Python and SQL
• Experience with feature engineering, model evaluation, and optimization techniques
• Experience working with large-scale data processing frameworks (e.g., Spark)
• PhD in a quantitative field
• Experience with operations research methods (e.g., linear programming, mixed-integer programming) and related tools
• Experience with NLP and text analytics
• Experience working with cloud platforms (e.g., AWS, Azure, or GCP)
• Familiarity with MLOps, CI/CD pipelines, and production model monitoring
• Experience with streaming or real-time data systems
• Strong understanding of data governance and responsible AI practices
• Ability to translate complex analytical results into business impact
• Excellent communication and collaboration skills

Company Overview

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