Background
I spent more than twenty years in the French Armed Forces, in roles spanning operational risk assessment, close protection, security training and team leadership — environments where the cost of misjudging a threat is immediate and real.
What that experience trained
Not a checklist, but a habit: read a situation quickly, separate what’s actually dangerous from what only looks that way, and decide with incomplete information. That habit doesn’t go away when the context changes — it’s the same judgment fraud and AML work asks for, applied to transactions instead of terrain.
Why data, now
Judgment alone doesn’t scale, and it isn’t easy to prove to someone who hasn’t seen it in action. SQL, Power BI and Python are how I’m turning that judgment into something structured, repeatable and explainable — not replacing it, but giving it a form an employer can actually evaluate.
Where the two meet
Most analysts can run a query. Fewer have had to decide, under pressure and with real stakes, whether something was actually a threat. That’s the gap this fills: knowing which signal is worth escalating, and being able to show the data behind that call.
Current training
Completing a Data Analyst certification at Wild Code School (RNCP Level 6), expected October 2026 — SQL, Power BI, Python and Pandas. See how this is applied in practice in the case study.