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# Data Scientist

- **Company:** Indotronix Avani Group
- **Location:** Chicago, IL, United States (Remote available)
- **Experience:** Expert
- **Contract:** Temporary contract
- **Skills:** Mxnet, Agile Methodology, Artificial Intelligence, Amazon Web Services, Data Analysis, Microsoft Azure, Big Data, Cloud Computing, Software Code Optimization, Computer Programming, Continuous Integration, IBM ILOG CPLEX Optimization Studio (CPLEX), Data Architecture, DevOps, Fraud Prevention and Detection, Python (Programming Language), Machine Learning, Tensorflow, SQL Databases, Data Streaming, Google Cloud, Feature Engineering, Pytorch, Large Language Models, Apache Spark, Deep Learning, Model Validation, Scikit Learn, Xgboost, Machine Learning Operations
- **Published:** October 5, 2026
- **Apply:** https://candidateportal.ceipal.com/job-details/KU4QzwqaQx1tm-5EmI1QEEvwcGzgmU3uTwpthBbqLrQ

## About the Role

Master's degree in computer science, statistics, industrial engineering, or related field (PhD preferred)

 - 5+ years in data science, operations research, or related area (2+ with PhD)
 - Deep expertise in insurance claims analytics, risk analysis, and fraud detection
 - Proficient in operations research modeling (LP, IP, MIP) and optimization tools (CPLEX, Gurobi)
 - Advanced skills in machine learning (supervised, unsupervised, deep learning), model evaluation, and feature engineering
 - Programming expertise in Python, SQL, Spark; strong command of ML frameworks (Scikit-Learn, XGBoost, Tensorflow, PyTorch, MXNet, LLM)
 - Cloud deployment experience (AWS, Azure, GCP) with large, complex datasets
 - Hands-on with streaming data architectures, Agile methodologies, DevOps, and CI/CD practices
 - Strong communication, documentation, and teamwork abilities

Preferred Skills:

 - Background working with data in hospitality, travel, or service industry settings
 - Advanced understanding of data architecture and MLOps best practices
 - Proven track record translating analytics into measurable business impact
 - Demonstrated passion for innovation and continuous learning in data science, Top 3 Must-Haves:* Insurance claims* Risk analysis* Fraud detectionRequired Skills -* 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 skillsPREFERRED SKILLS:* Exposure to ho 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 da

## Description

Join a cutting-edge analytics team as a Senior Data Scientist and drive transformative machine learning and NLP solutions for an industry-leading claims and incident mitigation project. Make a real impact by helping risk management teams predict, classify, and proactively address high-risk insurance claims. This is a fully remote opportunity offering exposure to large-scale data challenges, cloud technologies, and cross-functional collaboration-all while advancing your career in applied data science., Design and implement ML and NLP solutions for insurance claims and incident mitigation analytics

 - Translate complex risk management business needs into actionable data science strategies
 - Profile, clean, and prepare structured and unstructured claims and incident data for modeling
 - Engineer advanced features and leverage NLP to extract key risk signals from narrative data
 - Apply record linkage techniques to connect disparate data sources without unique identifiers
 - Build, validate, and deploy models ranking incidents by claim likelihood and severity
 - Classify claims by probable financial impact and assist in fraud detection
 - Collaborate closely with Risk Management, Legal, Data Engineering, BI, Data Governance, and MLOps teams
 - Monitor model performance, ensure scoring quality, and manage retraining cycles
 - Document assumptions, feature logic, validation outcomes, and model limitations
 - Present results and actionable insights to both technical and business stakeholders