Note: The job is a remote job and is open to candidates in USA. Forbes Technical Consulting is seeking a Senior Data Scientist for their Claims & Incident Analytics team. The role focuses on developing machine learning and NLP solutions to improve claims and incident mitigation analytics.

Responsibilities

• Translate risk management business requirements into well-defined data science solutions, including incident prioritization and claim severity classification
• Profile, clean, and prepare claims and incident data for modeling and scoring
• Develop feature engineering logic using structured and unstructured data sources
• Apply NLP and text-processing techniques to claim and incident narratives to extract risk signals
• Build record-linkage approaches to connect incidents and claims where clean unique identifiers are unavailable
• Build and validate models that rank incidents by likelihood of escalation or intervention needed
• Build and validate claim severity models that classify claims by financial impact and high-dollar risk
• Generate explainability outputs including key risk drivers and business-readable flags for incidents and claims
• Collaborate cross-functionally with Legal, Data Engineering, BI, Data Governance, and MLOps partners
• Monitor model performance, drift, scoring quality, and retraining needs
• Document modeling assumptions, feature logic, validation results, limitations, and handoff requirements
• Ensure appropriate handling of PII and sensitive data fields per governance standards
• Present findings and recommendations clearly to both business and technical stakeholders

Skills

• Expertise in operations research modeling (LP, IP, MIP) and solvers such as CPLEX or Gurobi
• Expertise in machine learning — supervised, unsupervised, and deep learning methods
• Expertise in feature engineering, model evaluation, and hyperparameter tuning
• Proficiency in Python, SQL, and Spark; experience with Scikit-Learn, XGBoost, TensorFlow, PyTorch, MXNet, and LLM frameworks
• Experience developing and deploying solutions in cloud environments (AWS, Azure, or GCP) with large datasets
• Experience with streaming data architectures and Agile methodology
• Familiarity with DevOps and CI/CD concepts
• Master's degree in Computer Science, Statistics, Industrial Engineering, or related field (PhD preferred)
• 5+ years of data science or operations research experience (2+ years with a PhD)

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