Senior Data Scientist

Job Summary

The Senior Data Scientist will design, develop, and implement advanced machine learning, natural language processing (NLP), and analytics solutions to support claims and incident mitigation initiatives. This role will partner with business and technical teams to identify high-risk incidents, predict claim severity, improve risk management processes, and deliver actionable insights through data-driven decision-making. The ideal candidate will have expertise in machine learning, operations research, predictive analytics, and large-scale data processing within cloud-based environments.

Key Responsibilities

• Translate business requirements into scalable data science solutions focused on risk analysis, claims prioritization, fraud detection, and claim severity prediction.

• Profile, cleanse, and prepare structured and unstructured data for modeling, analytics, and scoring activities.

• Develop advanced feature engineering techniques utilizing claims, incident, and operational datasets.

• Apply NLP and text analytics techniques to extract insights from claim and incident narratives.

• Design and implement record-linkage methodologies to connect related incidents and claims when unique identifiers are unavailable.

• Develop predictive models that identify incidents likely to become claims or require intervention.

• Build and validate claim severity models to estimate financial impact and identify high-risk claims.

• Develop machine learning solutions using supervised, unsupervised, and deep learning techniques.

• Collaborate with Risk Management, Legal, Data Engineering, Business Intelligence, Data Governance, and MLOps teams to deliver business solutions.

• Monitor model performance, drift, scoring accuracy, and retraining requirements.

• Document model assumptions, feature engineering approaches, validation results, and limitations.

• Present analytical findings, recommendations, and model performance results to business and technical stakeholders.

• Support deployment and operationalization of machine learning solutions in cloud environments.

• Contribute to continuous improvement initiatives involving analytics, automation, and predictive modeling.

Required Qualifications

• Master's degree in Computer Science, Statistics, Industrial Engineering, Data Science, or a related field.

• PhD in a related field is preferred.

• 5+ years of experience in Data Science, Operations Research, Machine Learning, or a related field.

• 2+ years of relevant experience may be considered for candidates with a PhD.

• Experience with insurance claims analytics, risk analysis, and fraud detection.

• Expertise in operations research methodologies, including Linear Programming (LP), Integer Programming (IP), and Mixed Integer Programming (MIP).

• Experience using optimization tools such as CPLEX, Gurobi, or similar platforms.

• Expertise in building machine learning models using supervised, unsupervised, and deep learning techniques.

• Strong experience with feature engineering, model evaluation, model validation, and hyperparameter tuning.

• Advanced proficiency in Python, SQL, and Spark.

• Experience with machine learning frameworks such as Scikit-Learn, XGBoost, TensorFlow, PyTorch, MXNet, and Large Language Models (LLMs).

• Experience developing and deploying solutions in cloud environments, including AWS, Azure, or Google Cloud Platform.

• Experience working with large-scale datasets and distributed data processing technologies.

• Experience with streaming data architectures and real-time data processing.

• Experience working in Agile development environments.

• Understanding of DevOps practices and CI/CD methodologies.

• Excellent communication, presentation, and collaboration skills.

Preferred Qualifications

• Experience supporting hospitality, travel, service industry, or customer operations analytics initiatives.

• Strong understanding of data architecture principles and MLOps best practices.

• Experience deploying and managing machine learning solutions in production environments.

• Proven ability to translate complex analytical findings into measurable business outcomes.

• Experience with model governance, monitoring, explainability, and lifecycle management.

• Passion for continuous learning and innovation in applied data science and machine learning.

• Experience working with cross-functional business and technical teams in enterprise environments.

Primary Skills

• Insurance Claims Analytics

• Risk Analysis

• Fraud Detection

• Machine Learning

• Natural Language Processing (NLP)

• Python

• SQL

• Spark

• Operations Research (LP, IP, MIP)

• CPLEX

• Gurobi

• Scikit-Learn

• XGBoost

• TensorFlow

• PyTorch

• Cloud Platforms (AWS, Azure, GCP)

• MLOps

• CI/CD

• Feature Engineering

• Predictive Analytics

• Large Language Models (LLMs)