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# Data Scientist - AI/ML & Advanced Analytics

- **Company:** Diverse Lynx LLC
- **Location:** Minnetonka, MN, United States (Remote available)
- **Experience:** Expert
- **Salary:** $114,400.0 - $124,800.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Big Data, Data Visualization, R (Programming Language), Statistical Hypothesis Testing, Python (Programming Language), Machine Learning, Natural Language Processing, Recommender Systems, Tensorflow, SQL Databases, Pytorch, Model Validation, Generative AI, Scikit Learn, Information Technology, Xgboost, Tools for Reporting
- **Published:** October 3, 2026
- **Apply:** https://www.careerjet.com/job/us979873fa9724819cae27822ce9e4ba29/eaa

## About the Role

· Bachelor's degree in Data Science, Statistics, Mathematics, Computer Science, Engineering, Economics, or a related quantitative discipline.
· 3+ years of experience in Data Science, Machine Learning, AI, or Advanced Analytics.
· Strong foundation in statistics, predictive modeling, and machine learning.
· Experience working with large-scale datasets.
· Proficiency in Python, SQL, R, or similar analytical languages.
· Experience with ML frameworks such as Scikit-learn, TensorFlow, PyTorch, XGBoost, or equivalent.
· Knowledge of data visualization and analytical storytelling.

## Description

· Analyze structured and unstructured datasets to identify trends, opportunities, and business insights.
· Develop and deploy predictive, classification, clustering, and Eligible to workimization models.
· Apply machine learning, statistical analysis, and AI techniques to solve business problems.
· Identify hidden patterns and relationships within complex datasets.
· Support experimentation, hypothesis testing, and model validation activities.
· Partner with business, operations, and technology teams to define analytical requirements and solutions.
· Translate business challenges into data science use cases and analytical frameworks.
· Generate insights and recommendations that improve operational performance and business outcomes.
· Develop reusable analytical assets, models, and reporting frameworks.
· Utilize AI/ML techniques including predictive analytics, anomaly detection, recommendation systems, NLP, and generative AI where appropriate.
· Monitor and evaluate model performance and recommend enhancements.