Data Scientist

CACI International•Lackland AFB, TX

2d•$93,500 - $196,500•Onsite

About The Position

The 35th Intelligence Squadron seeks a motivated AI/ML Engineer to develop and deploy complex Artificial Intelligence systems defending the Department of Defense Air Force Information Network (AFIN). The role requires anomaly detection algorithms for identifying and isolating malicious threats using supervised and unsupervised learning, Graph Neural Networks, and Deep Learning models. You will drive our cyber threat intelligence and detection mission by providing expert leadership and mentorship in advanced AI/ML solutions, augmenting cyber threat analysts triaging 1TB+ of boundary device logs daily to produce defensible intelligence reports with real mission consequences. You will provide technical direction for the design, implementation, testing, deployment, and operation of the 35 IS's cyber threat detection methods and enabling systems.

Requirements

Active TS/SCI security clearance.
DOD Directive 8140.01 (Security+ or equivalent).
5+ years in Machine Learning focused on deploying models into production.
Master's in Data Science, AI, or related quantitative field preferred.
Deep knowledge of ML frameworks such as PyTorch or TensorFlow.
Strong Python.
ML libraries including Spark MLlib, Scikit-learn, XGBoost, Keras, Hugging Face, PyTorch Geometric, MLflow.
Explainability tools such as SHAP, GNNExplainer, or attention-based interpretation methods.
Extensive experience incorporating data from multiple sources, labeling data for training, and identifying hidden patterns.
Matplotlib, Grafana, or Kibana.
Ability to communicate complex problems and solutions to non-technical leadership.

Nice To Haves

2+ years developing analytic solutions at scale over multiple PB of data.
Experience with Elasticsearch (ELK stack) and Lucene.
Docker/Kubernetes and containerized products.
CI/CD pipelines and Git.
Data pipelines - Pandas, NumPy, NiFi, Kafka, or similar.
Confluence, Jira, and collaborative platform experience.
Software engineering best practices across the full development lifecycle.
Familiarity with network defense, cybersecurity principles, and threat hunting.
Familiarity with data collection, storage, and monitoring.

Responsibilities

Architect, build, and deploy high-performance ML models with 1TB+ daily ingestion across heterogeneous data sources into production, ensuring scalability, reliability, and low latency.
Lead data preparation, model development, evaluation, monitoring, drift detection, and continuous retraining within mission-aligned constraints.
Develop and implement state-of-the-art algorithms, specifically a hybrid GNN and BiLSTM architecture operating on a continuously updated heterogeneous network graph.
Improve model performance through feature engineering, hyperparameter tuning, and advanced experimentation.
Mentor junior software developers and provide technical guidance and expertise.
Ensure appropriate documentation for all delivered analytics.
Build explainability into the product from the beginning to support defensible intelligence reports.
Ensure analyst trust is a design requirement, not an afterthought.
Guide analytic approaches where information is incomplete or no precedent exists.
Apply that experience to real constraints: classification boundaries, data that cannot be shared with vendors, upstream pipeline failures, and out of order log delivery.