Unknown Company
Senior Data Science Engineer (With TS/SCI Clearance and a current Full Scope Poly)
Annapolis Junction, United States | Posted on 08/11/2026
- 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.