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# Data Scientist Machine Learning Engineer

- **Company:** INPUT TECHNOLOGY INC
- **Location:** United States (Remote available)
- **Contract:** Permanent contract
- **Skills:** Data Cleansing, Information Engineering, Machine Learning, Natural Language Processing, Software Deployment, Model Validation, Machine Learning Operations
- **Published:** September 23, 2026
- **Apply:** https://arc.dev/remote-jobs/j/redirect/pm2y2fnrew

## About the Role

The ideal candidate will combine strong data preparation and engineering skills with hands-on experience in machine learning, supervised classification, model training, and evaluation. Experience working with NLP and multilingual datasets is highly desirable., * Strong experience in Data Science and/or Machine Learning Engineering.

 * Strong data engineering and data preparation experience, particularly with collection, normalization, and transformation of datasets.
 * Hands-on experience with machine learning model training, tuning, and experimentation.
 * Experience evaluating classifier performance and conducting false-positive/false-negative analysis.
 * Experience building reproducible ML workflows and pipelines.
 * Ability to clearly document technical methodology, progress, and results.
 * Strong analytical and problem-solving skills.

Nice to Have

 * Experience with NLP (Natural Language Processing).
 * Experience working with multilingual datasets.
 * Experience with supervised classification workflows.
 * Experience working with email data or similar text-based datasets.

## Description

* Collect and prepare raw email traffic and customer-reported samples across multiple languages.
 * Normalize and translate multilingual datasets into a common format suitable for model training.
 * Build reproducible data preparation and machine learning pipelines.
 * Train and fine-tune machine learning classifiers using labeled datasets.
 * Conduct experimentation and optimize model performance.
 * Evaluate models using holdout datasets and detailed performance analysis.
 * Analyze false positives and false negatives and identify opportunities for improvement.
 * Perform language-specific quality checks and overall model validation.
 * Document project methodology, progress, experiments, and results throughout the engagement.
 * Work closely with the client to deliver a validated model ready for production deployment.


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