Data Scientist / ML Platform Engineer

Peraton is a next-generation national security company that drives missions of consequence spanning the globe. They are seeking a Data Scientist / ML Platform Engineer to contribute across the full ML development lifecycle, focusing on applied data science and MLOps while collaborating with dedicated infrastructure engineers.

Responsibilities:

• 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

Requirements:

• 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
• 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