Hi M***y, I hope you’re doing well. I’m J***e, an HR recruiter at StrategyBrain working on behalf of F***d T***s. I came across your profile and was impressed by your sixteen years driving fraud prevention,and now transformation,from your leadership at T***o through to your current Director of Transformation role at V***n M***a O2. We’re looking for a Global Director of Fraud Transformation to define strategy, governance and KPIs, partner with tech and data science teams on AI-driven detection, and balance risk with growth across all channels. Based on your background, I’d love to see if this aligns with your goals. Would you be open to a brief chat about the role?

Hi M***y, Great to hear this aligns so well with your experience at V***n M***a O2 and T***o. Here’s a quick overview of F***d and the Global Director of Fraud Transformation role: • Company & Culture , F***d T***s is a global, mid-sized fintech innovator specializing in payment security and fraud prevention for top retailers and banks. We foster an agile, collaborative environment powered by advanced analytics and cross-functional teams. • Role & Responsibilities , Lead end-to-end transformation of fraud prevention capabilities across all business units , Define strategy, governance and KPIs to balance risk and growth , Oversee fraud risk assessments and intelligence programs (digital & physical channels) , Partner with tech, data science and operations to design and implement AI-driven detection solutions and agile processes , Align fraud controls with customer experience objectives and support global expansion • Impact & Growth , High-visibility role reporting into the C-suite, with the opportunity to build and scale a best-in-class fraud transformation program , Access to dedicated analytics, engineering and operations teams plus budget for emerging technologies , Clear roadmap for professional development, leadership mentoring and potential future roles within our global organization • Rewards & Benefits , Competitive base salary, performance bonus and equity options , Comprehensive benefits package, flexible working policies and continuous learning support I’d love to dive deeper into how your AI-driven detection work could shape our strategy. Are you available for a 30-minute call? Here are a few slots (all GMT): • Wednesday May 1st, 10:00,10:30 • Wednesday May 1st, 14:00,14:30 • Thursday May 2nd, 09:00,09:30 Please let me know which works best for you or share your preferred times. If you have any questions about the role or F***d, feel free to ask,I’m happy to clarify. Looking forward to chatting, J***e HR Recruiter, StrategyBrain (on behalf of F***d T***s)

Hi M***y, Great question. In this Global Director role, you’d have a core team of about 12,15 people reporting directly into you: - 3 Senior Managers (Fraud Analytics, Solutions Architecture, Transformation PMO) - 4,5 Data Scientists focused on model development and tuning - 4,5 Engineers responsible for real-time scoring infrastructure and API integrations Beyond your direct reports, you’d partner closely with a matrixed group of ~20 more: risk operations specialists, BI analysts, and regional fraud investigators across EMEA, APAC and the Americas. As for our tech stack, our main challenges right now are: 1. Legacy rules-based engines that struggle to scale globally,models are deployed unevenly across regions 2. Data silos between payments, loyalty and digital channels, which hinder holistic risk scoring 3. Limited real-time scoring capacity, leading to manual reviews and slower decisioning 4. A need to streamline deployment pipelines so AI models can go from lab to production in days, not weeks Your track record at V***n M***a O2 and T***o,building end-to-end AI-driven detection and breaking down data silos,would be exactly what we need to modernize our stack and accelerate our roadmap. Looking forward to diving deeper on Wednesday at 14:00 GMT. Please let me know if there’s anything else you’d like to cover before then. Best regards, J***e HR Recruiter, StrategyBrain (on behalf of F***d T***s)

Hi M***y, Great questions. 1. Cloud & MLOps stack • We run primarily on AWS, leveraging Kubernetes for container orchestration and Terraform for infrastructure as code. • Our MLOps pipeline is built around MLflow for experiment tracking, coupled with Kubeflow Pipelines for end-to-end orchestration and Jenkins/GitOps for CI/CD. This setup lets us push vetted models from development into production in a matter of days. 2. Data science organization • We have a core, centralized Data Science team (based in our London hub) focused on model development, tuning, and governance. • In parallel, smaller DS/analytics pods are embedded in each region (EMEA, APAC, Americas) to localize features, address market-specific data nuances, and work hand-in-glove with regional risk and operations teams. Let me know if you’d like more detail on any piece of the stack or org structure before our call on Wednesday! Best, J***e HR Recruiter, StrategyBrain (on behalf of F***d T***s)

Hi M***y, Great questions. We actually use a hybrid, centrally governed feature store (built on Feast in our AWS environment) where core features,think velocity metrics, device fingerprints, etc.,are defined, versioned (semantic tagging in Git), and governed in one place. Regional pods then contribute market-specific features via pull requests into that central repo, ensuring consistency while preserving local agility. As for lead time, our GitOps pipeline (Git → Jenkins/Cubeflow → Kubernetes) typically delivers a merged model or feature to live traffic in about 48,72 hours. For critical hotfixes we can tighten that to under 24 hours. Hope that helps,happy to dive deeper into our Feast setup or CI/CD workflows on Wednesday. Let me know if there’s anything else you’d like to cover before then. Best, J***e

Hi M***y, Great questions,here’s a high-level look at how we QA, roll out and audit features in our Feast-powered GitOps setup: 1. QA on Feast PRs • CI-driven validation: every PR kicks off Jenkins pipelines that run unit tests on your Python transforms, schema/type checks and automated data-validation tests (we use Great Expectations against a sample data snapshot). • Drift detection: we compare new feature distributions to baseline stats (via TFDV) and flag anything beyond preset thresholds before merge. • Peer review: once the automated gates pass, a fellow data-engineer or analyst does a quick sanity check on logic and doc completeness. 2. Canary & rollback strategy • Kubernetes rolling updates: our GitOps pipeline pushes the new feature-enabled microservices to a 10% traffic slice in EKS and monitors key metrics (latency, fraud-alert rates, error rates) via CloudWatch. • Automated rollback: if any metric breaches its SLA threshold within the canary window, the pipeline triggers an immediate rollback to the prior release,usually within minutes. 3. Feature lineage & auditing • Feast registry metadata: every feature version is recorded in the central store and tagged in Git with its commit SHA. • MLflow linkage: model artifacts reference specific Feast feature versions, so you can trace exactly which feature build went into each model. • Audit logs: AWS CloudTrail captures who approved each PR and when, and our Kubeflow metadata service logs end-to-end workflow details,from data prep to live scoring. Happy to unpack any of this further on Wednesday’s call. Let me know if there’s anything else you’d like me to prepare. Best, J***e HR Recruiter, StrategyBrain (on behalf of F***d T***s)

Hi M***y, Great questions,here’s how we handle both: 1. Tuning Canary SLA Thresholds - We start by mapping key fraud KPIs (fraud loss rates, false-positive ratios, decision latency) to business impact thresholds in partnership with our Risk Ops and Finance teams. - Initial canary limits are set based on historical baselines (e.g. ±5% fraud-loss variance or ±2% false-positive drift). These thresholds live in CloudWatch alarms during the 10% traffic slice. - Post-canary, we review real-world performance,running quick post-mortems on any alerts,and iteratively adjust tolerances to balance risk and customer friction. Over time, this tightens our window while avoiding unnecessary rollbacks. 2. Feast Registry: Deprecation & Branching - We leverage lifecycle tags directly in the Feast registry (statuses like “experimental,” “active,” “deprecated,” “archived”). - Deprecation process: when a feature’s due for retirement, we tag it “deprecated,” notify downstream consumers via our internal Slack channel, and start a 2-week soak period. After that, we tag it “archived,” remove it from the default feature list, and clean up storage via our archival scripts. - For branching, feature authors work off Git branches,each branch carries its own tags and metadata. Once a branch is validated and reviewed, it’s merged back into the main registry, and feature versions are bumped according to semantic conventions. Hope that gives you the clarity you need,looking forward to unpacking more on Wednesday at 14:00 GMT. Let me know if there’s anything else you’d like to cover beforehand! Best, J***e HR Recruiter, StrategyBrain (on behalf of F***d T***s)