Senior Data Scientist (TS/SCI with Polygraph Required)
Clearance: TS/SCI with CI Polygraph
- Solve Real Mission Problems: Apply data science to complex SIGINT and operational datasets where analytical findings directly support mission outcomes.
- Explore and Understand the Data: Investigate patterns, relationships, anomalies, and characteristics within large volumes of signal and sensor data.
- Influence Model Development: Develop features, analytical approaches, and model recommendations that improve signal identification and classification.
- Work Directly with Analysts: Partner with mission users to understand operational context, validate findings, and continuously improve analytical approaches.
- Shape BUCKAROO’s Analytical Capability: Help establish the metrics, datasets, methodologies, and analytical standards used to evaluate and improve the system.
- Exploratory Data Analysis: Analyze large and complex mission datasets to identify patterns, trends, anomalies, relationships, and emerging characteristics relevant to signal identification.
- Signal & Data Characterization: Evaluate signal attributes, metadata, historical observations, and associated mission data to identify discriminating characteristics and improve analytical understanding.
- Feature Development: Identify, develop, and evaluate meaningful features that improve classification, clustering, similarity analysis, and signal identification performance.
- Statistical & Machine Learning Analysis: Apply appropriate statistical, supervised, unsupervised, and semi-supervised techniques to investigate mission questions and develop analytical solutions.
- Model Experimentation: Design and conduct experiments to compare algorithms, features, datasets, thresholds, and analytical approaches before recommending solutions for operational implementation.
- Model Evaluation: Develop and assess performance metrics including precision, recall, false-positive rates, confidence, clustering quality, and other measures appropriate to mission objectives.
- Training Data Analysis: Evaluate training datasets for quality, completeness, representativeness, class imbalance, labeling consistency, bias, and other factors that may affect analytical or model performance.
- Trend Analysis: Develop analytical methods for identifying changes in signal characteristics, operational patterns, model performance, and population behavior over time.
- Analyst Collaboration: Work directly with SIGINT analysts and mission subject-matter experts to understand operational context, validate analytical findings, and incorporate analyst feedback into data science activities.
- Explainability & Communication: Translate complex statistical and machine learning findings into clear explanations, visualizations, metrics, and recommendations for technical and nontechnical stakeholders.
- Data Science Leadership: Mentor junior and mid-level data scientists, review analytical methodologies, and promote sound practices for experimentation, reproducibility, documentation, and scientific rigor.
- Cross-Functional Collaboration: Partner with AI/ML Engineers and Software Engineers to transition validated analytical methods and models into operational BUCKAROO capabilities.
- Exploratory data analysis
- Statistical modeling and inference
- Hypothesis testing
- Experimental design
- Regression and classification
- Clustering and dimensionality reduction
- Time-series and trend analysis
- Anomaly and outlier detection
- Probability and uncertainty analysis
- Supervised learning
- Unsupervised learning
- Semi-supervised learning
- Ensemble methods
- Classification and clustering techniques
- Model selection and performance evaluation
- Feature engineering and feature selection
- Model interpretability and explainability
- Python
- Pandas
- NumPy
- SciPy
- Scikit-learn
- Jupyter
- SQL
- Visualization libraries such as Matplotlib, Plotly, or similar tools
- Experience with TensorFlow, PyTorch, XGBoost, or similar frameworks is beneficial when appropriate to the analytical problem but is not the primary focus of this position.
- SIGINT data
- RF or waveform-derived data
- Sensor data
- Time-series data
- High-dimensional datasets
- Multi-source intelligence data
- Operational or mission-generated datasets
- Data quality assessment
- Training data evaluation
- Reproducible analysis
- Model validation
- Analytical documentation
- Data visualization
- Communicating technical findings
- Agile development environments
- Git and collaborative development workflows
Ten (10) or more years of progressive experience applying data science, statistics, machine learning, or advanced analytics to complex technical or mission problems.
- Performing exploratory analysis on large or complex datasets.
- Developing statistical or machine learning approaches to solve operational problems.
- Identifying and engineering meaningful features from complex data.
- Evaluating model effectiveness using quantitative performance measures.
- Working with incomplete, noisy, imbalanced, or imperfect datasets.
- Communicating analytical findings to technical teams, mission analysts, and decision-makers.
- Working collaboratively with software engineers or AI/ML engineers to transition analytical prototypes into operational capabilities.
- Experience supporting the Department of Defense, Intelligence Community, SIGINT mission, RF analysis, or similar mission environments is strongly preferred.
- Data Science
- Statistics
- Mathematics
- Applied Mathematics
- Operations Research
- Physics
- Engineering
- Computer Science
- or a related quantitative field
- Medical, Dental, and Vision Premiums 100% Employer Paid for you and your legal dependents or plus-up, cost-split plan.
- 401(k) with 6% Match.
- 11 Paid Federal Holidays.
- 120 hours of Paid Time Off (PTO).
- Company Outings and Trips.
- Tuition Reimbursement, Skillset Training, and New/Renewed Certification Assistance.
- HomeFundIt Company Down Payment Match — Employer match toward the down payment of buying a new home.