Insurance Claim Data Analyst Jobs (NOW HIRING)

PURPOSE:
The Sr Data Scientist will design and implement machine learning and NLP solutions for a claims and incident mitigation analytics project. This role will help risk management teams identify high-risk incidents earlier, classify claims by likely severity and financial impact, and provide explainable insights that support faster intervention. The position responsibilities outlined below are not all encompassing. Other duties, responsibilities, and qualifications may be required and/or assigned as necessary.

POSITION 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 analytics, modeling, and scoring.
• Develop feature engineering logic using structured and unstructured claims and incident data.
• Apply NLP and text-processing techniques to claim and incident narratives to extract useful risk signals.
• Develop record-linkage approaches to connect incidents and claims when a clean unique identifier is not available.
• Build and validate models that rank incidents by likelihood of becoming claims or requiring Risk Management intervention.
• Build and validate claim severity models that classify claims by likely financial impact and high-dollar claim risk.
• Generate explainability outputs, including key risk drivers and business-readable reasons for flagged incidents or claims.
• Collaborate with Risk Management, Legal, Data Engineering, BI, Data Governance, and MLOps partners to deliver usable business outputs.
• Monitor model performance, drift, scoring quality, and retraining needs.
• Document modeling assumptions, feature logic, validation results, limitations, and handoff requirements.
• Ensure data science work follows data governance expectations, including appropriate handling of PII and sensitive fields.
• Present findings, model results, and recommendations to business and technical stakeholders in a clear, actionable manner.

EXPERIENCE AND QUALIFICATIONS:

Required Skills -
• Strong experience building supervised machine learning models, especially classification, ranking, and severity / risk scoring models.
• Strong experience with data profiling, data cleaning, feature engineering, model validation, and model evaluation.
• Experience working with messy, sparse, real-world enterprise datasets.
• Strong Python and SQL skills.
• Experience with NLP or text analytics, including narrative cleaning, text classification, embeddings, keyword extraction, or summarization.
• Experience with probabilistic record linkage, entity resolution, fuzzy matching, or deduplication.
• Experience explaining model outputs using feature importance, SHAP, reason codes, or other explainability methods.
• Experience working with Snowflake or similar enterprise data warehouse platforms.
• Experience supporting batch scoring, model monitoring, and production handoff.
• Strong understanding of data governance, sensitive data handling, and PII masking or exclusion.
• Excellent communication and teamwork skills.

PREFERRED SKILLS:
• Experience with insurance claims, risk management analytics, litigation analytics, fraud detection, or safety analytics.
• Experience with claim severity modeling or high-dollar claim prediction.
• Experience with AWS, SageMaker, or similar cloud-based data science environments.
• Experience supporting BI outputs in Tableau, Power BI, Looker, or similar tools.
• Familiarity with MLOps best practices, model versioning, monitoring, and retraining workflows.
• Exposure to hospitality, property operations, guest experience, or enterprise safety data.
• Proven ability to translate complex analytics into practical business workflows and measurable impact.

EDUCATION:
Master's degree in computer science, statistics, data science, industrial engineering, operations research, mathematics, or related field preferred. Bachelor's degree with strong relevant experience acceptable.

5+ years of experience in data science, machine learning, risk analytics, or related area preferred.