Data science at Lockheed Martin in 2026: AI/ML roles, salary, and clearance requirements
Lockheed Martin is not the first name most data scientists think of when planning their careers. That is changing. The company has invested heavily in AI and machine learning capabilities across its four business areas, and it now employs over a thousand data scientists, ML engineers, and AI researchers working on problems that most tech companies will never encounter: predicting satellite component failures from telemetry streams, autonomously classifying objects in contested space environments, and optimizing supply chains that span classified and unclassified networks simultaneously.
The scale of data science at Lockheed Martin
Lockheed Martin's data science and AI workforce is distributed across four business areas: Aeronautics, Missiles and Fire Control (MFC), Rotary and Mission Systems (RMS), and Space. The Space business area, headquartered in Denver, is the largest employer of data scientists working on space-related problems.
The company established a centralized AI Center of Excellence (now integrated into its digital transformation organization) that provides shared tools, platforms, and best practices across all four business areas. Individual programs also maintain embedded data science teams that work directly on program-specific problems.
| Business Area | HQ | Key DS/ML Applications | Estimated DS Headcount |
|---|---|---|---|
| Space | Denver, CO | Satellite anomaly detection, orbit prediction, sensor processing | 350-450 |
| Aeronautics | Fort Worth, TX | Predictive maintenance, flight data analysis, digital twin | 250-350 |
| Missiles & Fire Control | Grand Prairie, TX | Targeting, autonomous systems, threat classification | 200-300 |
| Rotary & Mission Systems | Moorestown, NJ | Radar/sonar processing, C2 systems, cyber analytics | 250-350 |
| Corporate / AI CoE | Various | Platform development, research, enterprise analytics | 100-150 |
The Space business area is where the most distinctive data science work happens. Lockheed Martin operates satellites that generate terabytes of data daily, and extracting actionable intelligence from that data is a core competitive advantage. Space domain awareness, satellite health monitoring, ground system optimization, and mission planning all rely heavily on data science and ML capabilities.
Specific data science roles and what they pay
Lockheed Martin uses a standardized job architecture with levels ranging from early career (Level 1) to principal/fellow (Level 5+). Data science titles include Data Scientist, Machine Learning Engineer, AI Research Scientist, Data Engineer, and Analytics Engineer.
| Role | Level | Salary Range | Total Comp (with bonus) | Typical Experience |
|---|---|---|---|---|
| Data Scientist (entry) | Level 1 | $85K-$105K | $90K-$115K | 0-2 years, BS/MS |
| Data Scientist | Level 2 | $100K-$130K | $110K-$148K | 2-5 years |
| Senior Data Scientist | Level 3 | $125K-$165K | $140K-$190K | 5-10 years |
| Staff Data Scientist | Level 4 | $155K-$195K | $175K-$230K | 10-15 years |
| Principal Data Scientist | Level 5 | $180K-$220K | $210K-$270K | 15+ years |
| ML Engineer (entry) | Level 1 | $90K-$110K | $95K-$120K | 0-2 years, BS/MS |
| Senior ML Engineer | Level 3 | $130K-$170K | $148K-$198K | 5-10 years |
| AI Research Scientist | Level 3-4 | $140K-$200K | $158K-$240K | PhD + 3-8 years |
| Data Engineer | Level 2-3 | $100K-$155K | $110K-$178K | 3-8 years |
Lockheed Martin's data science salaries are competitive within the defense industry but 20-40% below equivalent roles at FAANG companies. The compensation gap narrows when factoring in Lockheed Martin's pension (worth $500K-$1M+ over a career), 401(k) match (up to 10% of salary), and annual bonus (typically 8-15% of base salary).
Predictive maintenance and digital twin applications
Predictive maintenance is one of the largest and most mature data science applications at Lockheed Martin. The company applies ML models to predict component failures, optimize maintenance schedules, and reduce unplanned downtime across its product portfolio.
For space systems, predictive maintenance means analyzing satellite telemetry data (temperatures, voltages, reaction wheel speeds, thruster valve cycles) to detect anomalies before they cause mission-impacting failures. A data scientist on this work might develop time-series anomaly detection models that process hundreds of telemetry channels in near-real-time, flagging deviations that indicate impending hardware degradation.
Digital twin technology takes this further by creating physics-informed computational models of entire spacecraft or subsystems. Data scientists work alongside domain engineers to build these models, which combine first-principles physics with data-driven corrections to predict system behavior under conditions that have never been observed.
| Application | ML Techniques | Data Sources | Impact |
|---|---|---|---|
| Satellite health monitoring | Time-series anomaly detection, LSTM, transformer models | Telemetry (thermal, power, attitude) | Prevents mission-ending failures |
| Component life prediction | Survival analysis, physics-informed neural networks | Test data, operational history | Extends satellite mission life |
| Supply chain optimization | Demand forecasting, reinforcement learning | ERP systems, supplier data | Reduces inventory costs 15-25% |
| Manufacturing quality | Computer vision, statistical process control | Inspection imagery, sensor data | Reduces defect escape rate |
| Ground system optimization | Scheduling algorithms, constraint satisfaction | Mission requirements, resource availability | Increases throughput 20-30% |
Security clearance requirements
The security clearance requirement is the single biggest differentiator between data science at Lockheed Martin and data science at a tech company. Understanding how clearances work is essential for anyone considering this career path.
| Clearance Level | Investigation Scope | Timeline | Percentage of LM DS Roles |
|---|---|---|---|
| None (unclassified) | N/A | N/A | 15-20% |
| Secret | 10-year background check | 3-8 months | 25-30% |
| Top Secret | Extensive background, polygraph possible | 8-15 months | 30-35% |
| Top Secret/SCI | TS + additional compartmented access | 12-18+ months | 20-25% |
Approximately 80-85% of data science positions at Lockheed Martin require some level of security clearance. The implication is that non-U.S. citizens are excluded from the vast majority of positions, and even U.S. citizens must pass an extensive background investigation.
Lockheed Martin will sponsor security clearances for new hires, meaning you do not need to already hold a clearance to apply. However, candidates with existing clearances are strongly preferred because they can start contributing to classified work immediately. The clearance investigation examines criminal history, financial responsibility, foreign contacts, substance use, and mental health history.
Tools, tech stack, and work environment
Lockheed Martin's data science tech stack has modernized significantly in recent years but still differs from the typical tech company environment in important ways.
| Category | Tools/Platforms | Notes |
|---|---|---|
| Languages | Python (primary), R, Julia, C++ | Python dominant; C++ for production ML on embedded systems |
| ML Frameworks | PyTorch, TensorFlow, scikit-learn | PyTorch gaining share over TensorFlow |
| Data Platforms | Databricks, Snowflake, internal Hadoop clusters | Mix of cloud and on-premises depending on classification |
| Cloud | AWS GovCloud, Azure Government | Air-gapped classified environments for TS/SCI work |
| MLOps | Kubeflow, MLflow, custom CI/CD | Less mature than tech company MLOps |
| Visualization | Tableau, Power BI, Plotly/Dash | Tableau dominant for enterprise reporting |
| Collaboration | GitLab (internal), Confluence, JIRA | On-prem GitLab for classified networks |
The biggest technical difference from a tech company is the classified network environment. On classified programs, data scientists work on air-gapped networks with no internet access. Software installation requires approval through a controlled software process. This means you cannot simply pip-install a new library; it must be evaluated, approved, and transferred through a security boundary. This process adds friction but also forces disciplined dependency management.
How to get hired as a data scientist at Lockheed Martin
Lockheed Martin recruits data scientists through campus recruiting, direct applications on its careers portal, and increasingly through AI/ML-specific recruiting events and hackathons.
For entry-level candidates, the most competitive profiles combine a master's degree in data science, computer science, or a quantitative field with an internship at a defense or intelligence community organization. Lockheed Martin's own internship program converts at approximately 50% for data science roles.
For experienced candidates, Lockheed Martin actively recruits from tech companies, national laboratories, and academic research groups. The company's recruiting pitch centers on unique problem domains (you will not find satellite anomaly detection datasets at Google), mission impact, and work-life balance compared to tech industry norms.
| Hiring Path | Typical Background | Time to Offer | Notes |
|---|---|---|---|
| Campus Recruiting | MS/PhD in CS, DS, stats, physics | 4-8 weeks | Target 20+ universities |
| Internship Conversion | LM intern with strong performance | 2-4 weeks | ~50% conversion rate |
| Experienced Hire (with clearance) | Defense/IC background, 3+ years | 3-6 weeks | Fastest path for cleared candidates |
| Experienced Hire (without clearance) | Tech company or academia, 3+ years | 6-12 weeks | Clearance sponsorship adds time |
| AI Fellows Program | PhD + research publication record | 8-12 weeks | Selective; research-focused roles |
Browse current Lockheed Martin jobs on Zero G Talent or explore all data science jobs in the space industry.
FAQ
Do I need a PhD to be a data scientist at Lockheed Martin?
No. A master's degree in a quantitative field is sufficient for most data science positions. A PhD is preferred for AI Research Scientist roles and provides access to the AI Fellows program, but the majority of Lockheed Martin data scientists hold MS degrees. Strong BS candidates with relevant internship or work experience are also hired.
Can I use open-source tools on classified networks?
Yes, but with restrictions. Open-source software must go through a review and approval process before installation on classified networks. Popular data science libraries (NumPy, pandas, scikit-learn, PyTorch) are generally pre-approved and available. Newer or less common packages may take weeks to months to get approved.
How does LM data science compare to working at a tech company?
The problems are more unique and arguably more impactful (satellite failure prediction, space object classification), but the tooling is less cutting-edge, the iteration speed is slower (especially on classified networks), and the salary is lower. Work-life balance is significantly better at Lockheed Martin, with standard 40-hour weeks and minimal weekend work.
Is remote work available for LM data scientists?
Hybrid work (2-3 days on-site) is standard for positions that involve unclassified work. Fully remote positions exist but are rare. Positions requiring access to classified networks are fully on-site by necessity since classified work cannot be performed outside approved facilities.
What locations are best for data science at Lockheed Martin?
Denver (Space), Fort Worth (Aeronautics), and the Washington DC metro area (corporate and multiple programs) offer the largest data science teams and the most diverse project opportunities. Orlando, Huntsville, and Moorestown NJ also have significant data science presence.
