Senior Data Science Engineer (With TS/SCI Clearance and a current Full Scope Poly)

Key Responsibilities

• Design, build, and deploy machine learning models and data science solutions in production.
  
• Develop scalable data pipelines for data ingestion, transformation, and feature engineering.
  
• Build and maintain end-to-end ML workflows, including model training, evaluation, deployment, and monitoring.
  
• Analyze large, structured, and unstructured datasets to identify trends and business opportunities.
  
• Collaborate with product managers, software engineers, data engineers, and business stakeholders to define data-driven solutions.
  
• Optimize machine learning algorithms for performance, scalability, and reliability.
  
• Implement MLOps best practices, including CI/CD pipelines, model versioning, automated testing, and monitoring.
  
• Ensure data quality, governance, security, and compliance with organizational standards.
  
• Mentor junior data scientists and engineers through technical guidance and code reviews.
  
• Stay current with emerging AI, machine learning, and cloud technologies and recommend innovative solutions.
  

Required Qualifications

• Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related field.
  
• 5–8+ years of experience in data science, machine learning, or AI engineering.
  
• Strong programming skills in Python (required); experience with SQL and one additional language (Java, Scala, or C++) is a plus.
  
• Experience with machine learning libraries such as scikit-learn, TensorFlow, PyTorch, or XGBoost.
  
• Strong understanding of statistics, predictive modeling, optimization, and feature engineering.
  
• Experience with data processing frameworks such as Spark or Hadoop.
  
• Proficiency in SQL and working with relational and NoSQL databases.
  
• Experience deploying machine learning models using cloud platforms (AWS, Azure, or Google Cloud).
  
• Familiarity with containerization and orchestration technologies such as Docker and Kubernetes.
  
• Experience with Git, CI/CD, and DevOps practices.
  
• Excellent analytical, communication, and problem-solving skills.
  

Preferred Qualifications

• Experience with generative AI, large language models (LLMs), retrieval-augmented generation (RAG), or AI agents.
  
• Knowledge of MLOps tools such as MLflow, Kubeflow, SageMaker, Vertex AI, or Azure ML.
  
• Experience with streaming platforms such as Kafka.
  
• Familiarity with vector databases and semantic search.
  
• Cloud certifications or machine learning certifications.
  
• Experience leading technical projects or mentoring engineering teams.