You're joining Arrivia's Security team, which protects the BuilderEx platform and its partner brands from fraud, abuse, and account compromise across multiple travel loyalty programs.
The problem you've been handed:
Over the past month, the security team has traced a new wave of successful logins to compromised loyalty accounts that current rule-based checks are missing. The pattern: credential-stuffing bots rotating through residential proxies, automation frameworks that mimic human timing closely enough to slip past simple thresholds, and rapid high-value redemption requests immediately after login.
You've been asked to build and evaluate an account-takeover / bot-abuse risk model — not a set of hand-written rules — that produces a risk score usable at login time.
Provided asset: account_takeover_events.csv — a synthetic, labeled dataset of ~420 login/session events. Each row represents one session with behavioral and network fields plus a label (is_account_takeover: 0 or 1). The positive class is intentionally rare (~12%), reflecting real account-takeover base rates.
Fields in the dataset:
We expect you to use AI tools. We evaluate how you use them — not whether you use them.
Evidence of iteration, redirection, and critical evaluation scores higher than a polished output with no process documentation.
The single highest-signal indicator: your video answer to the mandatory AI question. If you cannot name a specific moment where you redirected AI output, evaluators will assume you did not.
Mandatory AI question for your video: Walk me through one moment where you disagreed with, pushed back on, or redirected what the AI gave you — and what you did instead. Name the specific moment. Explain what the AI produced that didn't meet the bar, what you did differently, and why.
Speak naturally. Communication is assessed on clarity of technical ideas and logical structure — not verbal polish, accent, or filler words.
Submission: Upload each deliverable as a separate file directly on the Provn platform: your model code, your README document (Sections A, B, and C), and your video walkthrough (MP4 or MOV).
Demonstrate the ability to frame a security problem as an imbalanced-classification task and choose evaluation metrics that reflect real cost asymmetry between false positives and false negatives
Engineer a small set of features that are traceable to specific attacker behaviors, working within a fixed set of provided fields
Design a scoring approach that respects a real-time, low-latency production constraint
Show production engineering judgment: incident response thinking for a live precision-degradation scenario, appropriate to an on-call ownership model
Document architectural trade-offs, security reasoning, and AI collaboration process for both engineering and compliance audiences
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