Former Space Force Space Operations Officer (13S, Captain). I build decision tools out of messy data. I’ve connected 9 satellite-tracking and maintenance data sources into one operations picture, trained 120 operators to use it, and briefed general officers on the results. Python and SQL; built an AIP workflow for Palantir’s Build to Apply track.
- Scoped a vague “we can’t see sensor readiness” complaint into a data problem. I joined 9 sensor, maintenance, and tasking feeds into one readiness view that cut daily tasking decisions from 3 hours to 40 minutes.
- Found that 12% of tracking records carried stale timestamps from one legacy system. Wrote SQL validation checks that stopped bad data before it reached operators.
- Trained 120 operators across 4 sites on the new workflow and rebuilt it twice from their feedback. Daily usage reached 95% in 2 months.
- Briefed findings to 2 general officers. That led to a $1.8M decision to reposition sensor maintenance crews.
- Led a 6-person crew on 24/7 missile-warning operations, with zero missed or late reports across 300+ shifts.
- Built a Python script that parsed shift logs and flagged recurring anomalies. Engineers used it to fix 3 long-standing false-alarm sources.
- Rewrote the crew training plan with the instructors’ input, cutting certification time from 14 to 9 weeks.
- Loaded public space-track data into a Foundry ontology and built an AIP assistant that ranks close-approach alerts by risk and drafts operator notes. Submitted a 4-minute demo video.
Data:SQL, Python (pandas), Data modeling, Data quality checks, Dashboards
Palantir Platforms:Foundry (Developer Tier), AIP, Ontology basics, Pipeline Builder
Field Work:User interviews and workflow mapping, Operator training, Executive briefings, Requirements scoping