Data Analyst (Hybrid- Annapolis, MD)

Fairwinds Technologies, L

9h•$87,000 - $98,000•Hybrid

About The Position

The C-UAS Data Analyst serves as a core member of the DHS C UAS analytical cell, responsible for data ingestion, cleaning, fusion, visualization, statistical analysis, and performance assessment across a diverse set of sensors and systems—including RF detection, radar, EO/IR, C2 software, networking/backhaul, power systems, and deployment kits. You will help quantify detection, tracking, classification, latency, geolocation accuracy, system availability, and other mission critical performance indicators that support Developmental Test (DT), Operational Test (OT), field deployments, operator training, and leadership decision making. The role sits at the intersection of engineering, T&E, operations, cybersecurity, and program leadership, ensuring that data is trustworthy, traceable, repeatable, and actionable.

Requirements

Must be a U.S citizen with an ACTIVE security clearance of at least the SECRET level.
8+ years’ experience in data analysis, data engineering, data collection, or applied analytics, preferably in government, defense, aerospace, or complex technical programs.
Demonstrated experience analyzing sensor data, network logs, time‑series data, or multi‑modal datasets.
Strong knowledge of statistical analysis, data‑cleaning techniques, and performance characterization methods.
Experience building dashboards in Power BI, Tableau, or equivalent.
Ability to support fast‑paced test events, deployments, and operational missions with accuracy and composure.
Excellent written and verbal communication skills, capable of producing complex analysis products under tight deadlines.

Responsibilities

Collect, ingest, and manage data from multi sensor systems, C2 servers, network logs, time series telemetry, video streams, RF captures, and middleware APIs.
Build and maintain structured data pipelines using best practices for traceability, metadata tagging, version control, and chain of custody.
Validate data quality and flag issues to engineering and field teams.
Maintain and continuously improve the program’s centralized data repository, ensuring compliance with security, retention, and data handling policies.
Clean and normalize heterogeneous datasets for analytical use, including labeling, data wrangling, resampling, and sensor alignment.
Build and maintain data dictionaries, feature definitions, schemas, and transformation documentation.
Compute performance metrics such as: Probability of Detection (Pd), False Alarm Rate (FAR), Track continuity / track fragmentation, Latency and throughput, CEP / geolocation accuracy, Classification accuracy / confusion matrices, and System uptime, MTBF, MTTR.
Conduct exploratory data analysis (EDA), trend analysis, and comparative analytics across system versions, test events, sites, and environmental conditions.
Use statistical techniques to identify relationships, anomalies, degradation patterns, and emerging vulnerabilities.
Build dashboards and analytical products using tools such as Power BI, Tableau, Python, or custom visualization frameworks.
Produce compelling, executive ready visual reports for test events, readiness reviews, field deployments, and quarterly performance updates.
Communicate findings clearly to both technical and non-technical stakeholders, including operators, engineers, and program leadership.
Serve as the primary analyst during DT/OT events, capturing and processing on range data and generating quicklook reports.
Support field deployments with remote data verification, spot checks, and pre/post deployment analytic summaries.
Develop tools to accelerate repeatable data collection, synchronization, and analysis workflows.
Propose and implement automation improvements to reduce manual processing and enhance data reliability.
Contribute to the program’s long term data architecture, analytics strategy, and machine learning roadmap.