Senior Data Scientist

Job Post: Senior Data Scientist Industry: Hospitality & Travel Location: Chicago, IL (Downtown) – Hybrid (3 days onsite) Position Type: Contract Duration: ~4 months (Extension Likely) Pay Range: $90-$110/hr. C2C About Opportunity We are seeking an expert Senior Data Scientist for a premier, national hospitality brand to lead the design and deployment of sophisticated Machine Learning (ML), Natural Language Processing (NLP), and Operations Research (OR) models. This is a high-impact, project-based engagement focused entirely on a critical Claims and Incident Mitigation Analytics initiative. You will build the data-driven infrastructure that helps corporate Risk Management and Legal teams identify high-risk operational incidents early, classify claims by potential financial severity, and extract actionable risk signals from complex, unstructured narratives. Key Responsibilities & Deliverables As a Senior Consultant on this project, you will translate complex risk management requirements into production-ready data science solutions. Predictive Modeling: Build and validate models that rank operational incidents by their likelihood of escalating into legal claims, alongside claim severity models that classify potential financial impacts. NLP & Advanced Feature Engineering: Apply Natural Language Processing and text-processing techniques to unstructured claim and incident narratives to extract hidden risk signals. Data Preparation & Record Linkage: Profile, clean, and prepare disparate datasets; develop advanced record-linkage approaches to connect incidents and claims lacking clean unique identifiers. Explainable AI (XAI): Generate model explainability outputs, translating complex algorithmic decisions into business-readable risk drivers for non-technical stakeholders. Cross-Functional Collaboration: Partner closely with Risk Management, Legal, Data Engineering, BI, Data Governance, and MLOps teams to integrate and operationalize outputs. Production & Governance: Document all modeling assumptions, feature logic, and validation results. Ensure all models adhere to strict data governance, specifically the secure handling of PII and sensitive fields. Mentorship: Provide technical guidance and review work for junior team members on the project. Required Experience & Technical Qualifications To be successful in this role, you must bring a deep background in both predictive modeling and optimization, alongside the engineering discipline to handle large-scale data. Education: Master’s degree in Computer Science, Statistics, Industrial Engineering, Operations Research, or a highly quantitative field (PhD preferred). Experience: 5+ years of data science/operations research experience (2+ years if holding a PhD). Core Technical Stack: Advanced proficiency in Python, SQL, and Spark . Machine Learning Frameworks: Deep expertise across Scikit-Learn, XGBoost, TensorFlow, PyTorch, and LLM implementations. Operations Research: Hands-on experience with mathematical optimization modeling (LP, IP, MIP) and solvers like Gurobi or CPLEX . Cloud & Big Data: Proven experience developing and deploying models within a Cloud environment ( AWS, Azure, or GCP ) utilizing massive datasets and streaming data architectures. Methodology: Strong background operating within Agile frameworks, with an understanding of DevOps and CI/CD concepts. Preferred Attributes Prior exposure to handling risk management, legal, compliance, or insurance claims analytics. Industry experience within hospitality, travel, cruise, or large-scale service sectors. A strong understanding of data architecture and MLOps best practices for monitoring model drift and scoring quality. Why Take This Engagement? This project offers the opportunity to own a highly visible, end-to-end data science solution from data ingestion through to business adoption. You will see the direct financial and operational impact of your models in a world-class organization. Note to Candidates: This is a confidential search. Client identity will be disclosed to qualified candidates during the initial technical screening. Show more Show less