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# Sr Data Scientist Engineer

- **Company:** DeNOVO Solutions
- **Location:** Aurora, CO, United States
- **Experience:** Expert
- **Salary:** $145,000.0 - $175,000.0
- **Contract:** Permanent contract
- **Skills:** A/B Testing, Agile Methodology, Artificial Intelligence, Artificial Neural Networks, Confluence, Code Review, Collaborative Software, Data Infrastructure, Distributed Computing Environment, Monitoring of Systems, Machine Learning, Tensorflow, Jupyter Notebook, Supervised Learning, Data Processing, Data Ingestion, Pytorch, Delivery Pipeline, Apache Spark, Git, Scikit Learn, Information Technology, Deployment Automation, Xgboost, Dask, Machine Learning Operations, Devsecops, Docker, Unsupervised Learning
- **Published:** August 27, 2026
- **Apply:** https://www.clearancejobs.com/jobs/9120770/sr-data-scientist-engineer

## About the Role

Experience: Ten (10) to fifteen (15) years of experience designing, training, and deploying ML models within
the DoD or Intelligence Community, including experience leading technical teams or enterprise AI initiatives.
Education:
Bachelor's degree or higher in a quantitative discipline (e.g., statistics, mathematics, operations
research, engineering, or computer science).
A Master's degree may substitute for two years of experience; a PhD may substitute for four years.
An additional two years of related experience may substitute for a Bachelor's degree.
Certifications: None required.
Clearance: TS/SCI with CI Polygraph.

## Description

DeNOVO Solutions is seeking a Senior Data Scientist Engineer to lead the design, development, and deployment
of advanced AI/ML solutions supporting complex mission and operational environments. This role is ideal for an
experienced data science professional who thrives at the intersection of research, engineering, and mission
execution-developing high-impact, explainable models that transform raw data into actionable intelligence. You'll
guide the evolution of production ML systems, mentor mid-level engineers, and work directly with analysts to ensure
model performance meets the highest standards of accuracy, transparency, and operational readiness.

Why You'll Love This Role:

You'll Shape AI for the Mission: Design and deploy models that extract real-world value from signal and sensor
data.
You'll Drive Scalable ML Systems: Lead the integration of automated retraining and deployment pipelines
within secure cloud environments.
You'll Mentor and Lead: Share your expertise with junior data scientists and engineers, fostering a culture of
innovation and technical excellence.

A Day in the Life:

AI/ML Solution Development: Lead the creation and optimization of advanced ensemble, neural network,
and unsupervised learning models tailored to mission data.
Automation & Scalability: Design automated pipelines for data ingestion, curation, and retraining to ensure
model adaptability and long-term performance.
Validation & Explainability: Coordinate with analysts and mission experts to validate models, track accuracy
metrics, and enforce compliance with explainability and transparency standards.
Model Deployment: Integrate and operationalize ML models into production systems, collaborating with
DevSecOps to align with CI/CD pipelines and mission constraints.
Monitoring & Optimization: Implement tools for continuous monitoring, A/B testing, and drift detection to
maintain model reliability.
Team Leadership: Mentor junior team members, review code and models, and promote best practices in data
science methodology, governance, and reproducibility.

Tools, Technologies, Skills, & Knowledge:

Machine Learning & AI: Ensemble models, CNNs, RNNs, Transformers, unsupervised and semi-supervised
learning
Frameworks & Libraries: TensorFlow, PyTorch, Scikit-learn, XGBoost
Data Infrastructure: Feature stores, distributed computing frameworks (Spark, Ray, Dask)
Deployment & Monitoring: MLflow, DVC, Docker, model monitoring, A/B testing, data drift detection
Analytical Expertise: Causal inference, signal data processing, experiment design, model interpretability
(SHAP, LIME)
Collaboration Tools: Jupyter Notebooks, Git, Confluence, Agile/DevSecOps environments


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