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# Senior Data Scientist, Applied ML

- **Company:** REMOTE HAND
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Salary:** $154,000.0 - $200,000.0
- **Contract:** Permanent contract
- **Skills:** Airflow, Amazon Web Services, Cloud Computing, Data Validation, Information Engineering, Data Security, Monitoring of Systems, Intrusion Detection and Prevention, Python (Programming Language), Machine Learning, Natural Language Processing, Named Entity Recognition, Tensorflow, Unstructured Data, Feature Engineering, Pytorch, Apache Spark, Pandas, Containerization, Scikit Learn, Xgboost, Machine Learning Operations, Document Classification, Software Version Control
- **Published:** August 9, 2026
- **Apply:** https://arc.dev/remote-jobs/j/redirect/paeo2lz4tj

## About the Role

* Minimum 4 years of experience building and deploying models with end-to-end data lifecycle ownership.

 * Strong knowledge of applied mathematics including linear algebra, optimization, and statistics.

 * Experience with natural language processing techniques for text classification, tagging, or entity extraction.

 * Proficiency in Python and ML libraries such as PyTorch, TensorFlow, scikit-learn, and XGBoost.

 * Experience building or maintaining data and feature pipelines using tools like Airflow, Spark, or Pandas.

 * Familiarity with model versioning and monitoring tools such as MLflow or DVC.

 * Experience deploying models in cloud or containerized environments.

 * Strong communication skills to translate complex problems into actionable solutions.

## Description

The Senior Data Scientist, Applied ML role focuses on designing, building, and deploying machine learning models that support critical cybersecurity use cases. This position involves full ownership of the model lifecycle, from data preparation to production deployment, and collaboration with engineering, product, and research teams. The role is essential for advancing the organization''s security features through scalable and reliable systems that detect incidents, assess risks, and mitigate fraud.

 3. Responsibilities:

 * Develop, train, and deploy models using structured and unstructured data for security features such as threat detection, risk scoring, and classification.

 * Build preprocessing and feature engineering pipelines necessary for model performance.

 * Own model monitoring, evaluation, and the design of feedback loops to improve accuracy continuously.

 * Prototype new approaches and transition research prototypes to production-grade systems.

 * Ensure data validation, transformation, and pipeline health across research and production boundaries.

 * Collaborate with software and data engineers to deploy models in cloud-native environments like AWS.

 * Partner with product managers and domain experts to define success criteria and rapidly prototype new features.

 * Manage data access, transformation, and validation in collaboration with the data engineering team.

 * Document model design, tradeoffs, and outcomes clearly for technical and non-technical stakeholders.

 * Participate in model and compliance reviews and customer-facing discussions as needed.


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