Peraton
Data Scientist / ML Platform Engineer
Remote Data Science role with clear candidate location fit.
PostedJul 13, 2026
Eligible countries1 accepted country
Seniority signalMiddle
Work settingRemote
Accepted candidate locations
USA
Role overview
Data Scientist / ML Platform Engineer
Requirements and responsibilities
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What you'll do:
- Develop, train, and evaluate ML models (classification, regression, clustering, anomaly detection) and contribute to LLM-based capabilities such as RAG pipelines and prompt evaluation.
- Support model governance and deployment practices using MLFlow, including experiment tracking, model versioning, registry promotion workflows, and automated testing across the ML lifecycle.
- Contribute to production ML operations: model performance monitoring, drift detection, automated alerting, and incident escalation to maintain reliability and SLA compliance.
- Build and improve model serving infrastructure, feature pipelines, and lifecycle automation to support reproducible, scalable model development and inference.
- Apply explainability techniques (e.g., SHAP, LIME) and produce technical documentation to support stakeholder transparency and compliance requirements.
- Contribute to data ingestion, ELT/ETL transformation, and pipeline reliability using Spark and SQL-based frameworks within Snowflake and Databricks environments.
- Support pipeline orchestration, medallion architecture conventions, and data stewardship practices (metadata management, PII handling, lineage tracking in Unity Catalog).
- Perform occasional system administration tasks in collaboration with platform teams, including environment configuration, access management, compute troubleshooting, and secrets handling using platform-native tools.
Basic Qualifications:
- Associate's with 6 years, or Bachelor's degree with 4+ years of relevant experience, or Master's degree with 2+ years of relevant experience or High School diploma with 8 years of experience in lieu of a degree.
- Demonstrated experience with SQL and Python, including Python-based ML frameworks (e.g., scikit-learn, XGBoost, PyTorch, or TensorFlow).
- Hands-on experience with MLFlow or equivalent tools for experiment tracking, model governance, and lifecycle management.
- Strong understanding of SDLC fundamentals and experience with GitHub or equivalent version control.
- Experience with distributed compute environments (e.g., Spark, Databricks) and cloud-native services.
- Basic proficiency with Bash or shell scripting for automation and environment setup.
- Ability to collaborate across multidisciplinary teams and communicate technical concepts to varied audiences.
- Ability to obtain and maintain a Public Trust clearance
- US citizenship required or Green Card holder and must have been in the USA for 3 of the last 5 years.
Preferred Qualifications:
- Experience with MLOps practices including CI/CD for ML, containerization, feature pipeline automation, and model deployment frameworks.
- Experience with Databricks E2 components (Unity Catalog, Feature Store, Delta Live Tables) and/or model serving and drift monitoring tools (e.g., Databricks Model Serving, Evidenly, etc.).
- Experience with LLM frameworks (e.g., LangChain, LlamaIndex, Hugging Face Transformers) and familiarity with model explainability libraries (e.g., SHAP, LIME).
- Advanced Spark performance optimization experience and/or API development using Databricks REST APIs.
- Experience with healthcare analytics data (preferably Medicare or Medicaid) and familiarity with HIPAA or FedRAMP compliance constraints.
- Experience building data pipelines in a Snowflake or Databricks environment.
- Familiarity with orchestration tools (Airflow, Databricks Workflows).
- Exposure to streaming data patterns using Spark Structured Streaming, Delta Live Tables, or Kafka.
- Familiarity with environment reproducibility tooling (Docker, conda) and scripting (Python, Bash) to support automation and CI/CD tasks
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