I grew up in a military town. So, when I got a chance to do data science on a defense contract, I said yes. I was curious. I was nervous too. Big stakes. Lots of rules. But real people depend on the work. That weighed on me, in a good way.
This isn’t a hype piece. It’s a review from the inside—what I used, what helped, what hurt, and what I’d tell a friend who’s thinking about it. Here’s that full, unfiltered play-by-play if you want even more context.
What I actually did all day
Most days, I sat in a secure room with no windows. We call it a SCIF. No phones. Lots of coffee. My tools were simple but locked down: Python in Jupyter, scikit-learn, pandas, and sometimes XGBoost. We ran jobs on air-gapped servers. For search and logs, we used Splunk and sometimes ELK. For maps, it was ArcGIS Pro. For data join work and cases, I used Palantir Gotham on a classified network. Not fancy, but steady.
Did I miss cloud stuff? Sure. But we also used AWS GovCloud and Azure Government on some projects. It depended on data rules. When we had those, life felt lighter. On one pilot we even tried outsourcing part of the pipeline to a DSaaS platform—here’s my candid report on how that experiment went.
For a deeper look at the radio-frequency data flows that often feed these secure networks, check out the practical primers on vhfdx.net.
Real examples that stuck with me
Here are four cases I actually worked on. Nothing sensitive here. Just the kind of work we all did.
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Predictive maintenance on aircraft parts
We looked at flight hours, sensor readings, and work orders. One model flagged hydraulic pump issues a day or two before they failed. We used a tree model (random forest). We trained on old work orders and sensor spikes. The model wasn’t perfect. But it cut surprise failures by about 20% over one quarter. That meant fewer grounded planes and fewer late-night scrambles. A crew chief told us, “You gave us a heads-up. That’s gold.” I saved that note. -
Supply and parts demand for ground vehicles
We built a forecast for brake pads and filters on a set of trucks and MRAPs. We used simple features: miles since last service, environment (dusty or clean), and unit tempo. We tried fancy models, but a plain gradient boosted model plus a seasonal baseline won. It reduced stock-outs in one unit’s motor pool by a small but real slice. Think one fewer week with a truck stuck on blocks. Not sexy. But real. -
Computer vision for storm damage mapping
After a big storm hit near a base, we used drone images to map debris and flooded roads. We used a small TensorFlow model to spot downed trees and washed-out spots. It was fast and rough, not a science fair. We pushed a heat map to ArcGIS. Civil engineers used it to plan routes. One captain said it saved a morning of guesswork. The model missed a few things in shadows, so we added a quick human check. That combo worked best. -
Cyber alert triage
We helped a blue team tune alerts. This task lines up neatly with the DoD’s official Data Scientist role definition in the Cyber Workforce Framework. We pulled Windows event logs and NetFlow into Splunk. Then we used a light anomaly model (isolation forest) to rank weird spikes. The goal wasn’t “catch everything.” It was “cut the noise.” We dropped low-value alerts by about a quarter. The team could breathe. And when they breathe, they hunt better.
If you want to see other defense data-science efforts, the Naval Postgraduate School keeps a living catalog of experience projects that feels a lot like the work described above.
None of this was magic. It was careful. It was many small wins.
What I loved
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Clear mission
I knew why the work mattered. When a maintainer used our dashboard, I felt it. It wasn’t a vanity chart. It kept a jet in the air or a truck rolling. -
Strong teammates
We had smart analysts and salty NCOs in the same room. They called out bad ideas fast. They also taught me field reality. I stopped chasing pretty metrics and built tools they could use between tasks. -
Stable data streams (mostly)
Maintenance logs and sensor data came in steady. Not clean, but steady. That’s rare. Once we set up a pipeline, life got simpler week by week.
What was hard (and sometimes painful)
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Security slows you down
Every library had to get approved. A simple upgrade could take weeks. I learned patience. I also learned to write plain Python and not rely on wild stacks. -
Messy labels and shifting names
The same part could be logged five ways. One shop called it “HYD PUMP,” another “Hydraulic Pump #2.” We built a small rules engine and a fuzzy match step. It worked, but it was a grind. -
Model drift from policy changes
A new maintenance policy rolled out and boom—our base rates changed. Good policies, new patterns. The model fell off for a while. We retrained and set up monthly checks. It’s like mowing the lawn. It grows back. -
Vendor lock and brittle dashboards
Some tools were great until they weren’t. A small schema change broke three dashboards. We learned to export plain CSVs and keep a simple “last resort” view in Jupyter. -
Ethics fatigue is real
The stakes are high. I asked myself, “Is this fair? Is it clear?” We always had a human in the loop. We never auto-blocked or auto-grounded anything. We flagged. A person decided. That helped me sleep.
Tools I reached for, and why
- Python, pandas, scikit-learn: solid and readable. Easy to hand off.
- XGBoost: strong on tabular data. Just watch the versioning.
- TensorFlow Lite models for edge cases: small and fast.
- ArcGIS Pro: good maps, good sharing on secure nets.
- Palantir Gotham: data fusion and cases. Handy for joined views.
- Splunk: fast log search, alert logic, role control.
- Jupyter: my scratch pad. I kept clean notebooks as living docs.
If you’re curious about the ruggedized sensors and Data Sciences International gear that sometimes rode shotgun on these projects, I wrote up an honest take on that hardware as well.
I wanted fancy deep nets. Often, a simple tree won. Simple models travel better on closed systems. They’re easier to explain to a crew chief who has 12 minutes and a busted truck.
How we proved value without fancy words
We used three numbers most: precision, recall, and time saved.
- Precision said, “When we flag, how often are we right?”
- Recall said, “How many real issues did we catch?”
- Time saved was the big one. Did we cut guesswork? Did we prevent a scramble?
We set small targets across a quarter. Not year-long dreams. Small targets moved morale. Then we aimed again.
A quick story that changed my mind
One night, a pump alert fired. We were not sure. The score was right on the line. The maintainer said, “Show me the why.” We had SHAP values ready. They showed a jump in vibration plus a pressure dip. He checked the part. It was worn thin. He swapped it. No emergency later.
That’s when I stopped chasing black-box wins. If I can’t show the why, I don’t trust it. And if they can’t see the why, they won’t trust it either.
Culture notes you might not hear in a sales deck
- Humor keeps teams sane. We had sticker wars on laptops.
- Docs matter. Short, clear runbooks beat long ones no one reads.
- “Ship it” means something. But ship safe. You can move fast and still do the right checks.
- Bring donuts on release day. It sounds silly. It helps.
Every base town has its own off-shift social scene. If you ever rotate through central Florida and want a quick primer on where to unwind beyond the standard on-post rec center, check out this field-tested guide to Skip the Games Winter Haven alternatives which maps out low-drama meetups, vetted venues, and sensible safety tips for newcomers looking to make the most of their downtime.
Because we operated on closed networks, we occasionally considered using public chat rooms for quick Q&A when we were off shift, but I quickly learned that the typical “free chat website” comes with more risk and friction than value — here’s a no-BS breakdown of why most free chat sites fall short that shows the spam, security holes, and UX traps to watch out for if you’re tempted to rely on them.
Who should work in this space?
If