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# Data Scientist

- **Company:** BMO Harris Bank National Association
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Salary:** $80,000.0 - $175,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Microsoft Azure, Computer Programming, Continuous Integration, Python (Programming Language), Machine Learning, Recommender Systems, Tensorflow, Search Technologies, SQL Databases, Unstructured Data, Cloud Platform System, Feature Engineering, Pytorch, Transfer Learning, Large Language Models, Deep Learning, Model Validation, Keras, Scikit Learn, Information Technology, HuggingFace, Machine Learning Operations, Data Pipelines
- **Published:** September 5, 2026
- **Apply:** https://arc.dev/remote-jobs/j/redirect/phpytn04se

## About the Role

* 5+ years of hands-on experience in Machine Learning, AI, or Data Science.
 * Strong programming skills in Python and SQL.
 * Experience with PyTorch, TensorFlow, Keras, Scikit-learn, and Hugging Face.
 * Strong knowledge of machine learning, deep learning, statistics, and model evaluation techniques.
 * Experience with recommendation systems, predictive modeling, and large-scale customer analytics.
 * Experience working with embeddings, vector search, and modern AI/LLM technologies is an asset.
 * Strong problem-solving, communication, and collaboration skills.

Preferred Qualifications

 * Experience developing ML models in cloud environments (AWS, Azure, or GCP).
 * Experience with CI/CD, MLOps, and production ML systems.
 * Graduate degree (Master's or PhD) in Computer Science, Engineering, Statistics, Mathematics, or a related field.

This role is ideal for someone passionate about building scalable AI solutions and delivering measurable business impact through machine learning and advanced analytics.

## Description

* Design, develop, and deploy machine learning and deep learning models at scale.
 * Build recommendation and personalization systems using large structured and unstructured datasets.
 * Develop and optimize data pipelines, feature engineering processes, and ML workflows.
 * Work with pre-trained models, embeddings, transfer learning, and model fine-tuning techniques.
 * Conduct experiments, evaluate model performance, and continuously improve model accuracy.
 * Collaborate with business and technology teams to translate business problems into AI solutions.


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