Data Scientist

The Senior Data Scientist will design and implement AI, Machine Learning, and Operations Research models that transform business objectives into data-driven solutions. This role advances the mission by optimizing decisions, improving operations, and enhancing guest experiences through applied analytics and innovation. 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 business problems in a variety of business areas into well-defined data science projects, ensuring alignment with business goals, scope, and defined KPIs.

• Design, implement, and optimize advanced machine learning and optimization models to address complex business challenges.

*Collaborate with cross-functional teams, including engineering, data, and business stakeholders, ensuring clear communication, seamless integration of data-driven solutions.

• Monitor model performance in production, refining algorithms and processes to adapt to real-world data and evolving business needs.

• Create and maintain detailed documentation for models, methodologies, and workflows to support team knowledge-sharing.

• Conduct testing and validation of models to ensure robustness, scalability, and reliability in production environments.

• Present data-driven insights, findings, and product outcomes to stakeholders in a clear, actionable manner.

• Stay updated on the latest advancements in machine learning and optimization, integrating innovative techniques and tools into projects.

• Mentor junior data scientists by providing technical guidance, reviewing work, and fostering their professional development.

• Demonstrate a commitment to ethical data science, ensuring models and solutions are developed with fairness, transparency, and integrity., * Translate risk management business requirements into well-defined data science solutions, includin

g 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.

Deliverables

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. *, Manufacturing Data Analyst Employment Status: Full-Time, Salaried/Exempt Location: Waukegan, IL Schedule: Onsite daily Monday through Friday from 8 am - 5 pm CT. Salary: $75,000 - $95,000 Position Overview The Manufacturing Data Analyst will enhance su... Recommended *, Position: Safety & Security Coordinator Reports To: Plant Manager Salary: $80,000 - $110,000 Location: Fort Worth, Texas Job Description We are seeking a highly organized and detail-oriented individual to join our team as an EHS and Security Coordinato... Recommended *, Title: FSQA Manager - Sauces and Condiments Type: Direct Hire Salary: $118k with bonus potential Remote Status: 100% Onsite Hours: Standard is 8am-5pm. Why is the role open? Expansion of team Availability to Interview: Interview Process: 3 or 4 rounds ... Recommended *, Position Title: BIM Designer Location: Onsite first month for training (flexible hours), then hybrid in downtown Chicago Duration: Contract-to-Hire 6 months Salary: $80,000-$90,000 Required Tech/Experience (Manager won't consider candidates without the... Recommended *, Position: EHS Manager Location: Danville Metro Area, VA Salary: $100,000 - $120,000 / Annually Benefits: Healthcare, Medical, Dental, 401k, PTO Position Summary: The EHS Manager is responsible for leading site-wide environmental, health, safety, and pr... 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Position is not eligible for sponsorship. Benefits: Health, Medical, Dental, Vision... Recommended *, Title: Chemical Engineering Analyst Location: Sardis, MS Base Salary: $50,000-$70,000. Company Overview: Our client is a rapidly growing, vertically integrated manufacturer serving the regenerative and sustainable farming sectors. Headquartered in the ... Recommended *, Job Title: Sr Director of Process Engineering & Operations Houston Metro Area Travel : Up to 50% Job Type: Full-Time Exempt Compensation: $200-250k Benefits: This position is eligible for medical, dental, vision, 401(k), bonuses & PTO. Our client is se... Recommended *, * Translate risk management business requirements into well-defined data science solutions, includin

g 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.

Deliverables

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.

• Expertise in operations research modeling (LP, IP, MIP) and tools (CPLEX, Gurobi, etc).

• Expertise in building machine learning models, including supervised, unsupervised, and deep learning methods.

• Expertise in feature engineering, model evaluation, and hyperparameter tuning.

• Expertise in Python, SQL, and Spark, and a broad array of machine learning frameworks (Scikit-Learn, XGBoost, Tensorflow, PyTorch, MXNet, LLM, etc).

• Experience in developing and deploying solutions in a Cloud environment (AWS, Azure, GCP) with large datasets.

• Experience with streaming data architectures.

• Experience operating in an Agile Methodology environment.

• Experience with DevOps and CI/CD concepts.

• Excellent communication and teamwork skills.

PREFERRED SKILLS:

• Exposure to hospitality, travel, or service industry data and optimization use cases.

• Strong understanding of data architecture and MLOps best practices.

• Proven ability to translate complex analytics into business impact.

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

EDUCATION:

Master's degree in computer science, statistics, industrial engineering, or related fields required, PhD preferred

5+ years of experience in data science, operations research, or related area (2+ years for candidates with PhD).

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