Finom

Brand Manager

• Own Finom's visual identity, including brand systems, guidelines, design systems, and documentation
• Drive consistent visual identity adoption across teams and touchpoints
• Set creative direction and maintain high standards for consistency and quality
• Lead and develop the Marketing Design team and assess future staffing needs
• Define design requirements for intake, briefing, prioritization, SLAs, and review
• Design and direct visual and motion assets
• Build an AI-native production pipeline with quality and brand-integrity gates
• Create templates, systems, and tools for designers and AI workflows
• Provide creative direction for major projects from kickoff through concept and narrative
• Direct mood boards, design references, scamps, storyboards, and animatics
• Partner with Marketing, Product, copywriters, and strategists on digital-first campaigns
• Own projects from concept through delivery across multiple parallel workstreams
• Present creative work and rationale to senior stakeholders
• Manage freelance and external creative partners
• Act as guardian of the visual identity while pushing the brand creatively
• Audit the existing identity, team, and workflows and establish foundations for the next evolution

Art Director, Brand & Marketing Design

Machine Learning Engineer

• Develop full-stack machine learning models, including binary classification and regression, for financial crime detection and prevention during transaction processing and client onboarding
• Gather requirements, prototype, and deploy models in production
• Own and evolve the Dynamic Risk Score (DRS) across all markets, scoring the full client base daily
• Support decision-making across the client lifecycle, including quarantine, alert suppression, and future credit decisions
• Manage transaction rules with ML-driven detection and optimize performance and alert volumes
• Establish continuous production monitoring for model and rule performance
• Develop a champion-challenger framework to keep the best-performing model live
• Participate in infrastructure development for ML operationalization and inter-service interactions
• Serve and infer models of varying complexity

Senior Machine Learning Engineer

• Develop full-stack machine learning models, including binary classification and regression, for financial crime detection and prevention during transaction processing and client onboarding
• Gather requirements, prototype, and deploy models in production
• Own and evolve the Dynamic Risk Score (DRS) across all markets
• Score the full client base daily to support decisioning across the client lifecycle
• Manage transaction rules with ML-driven detection and optimize performance and alert volume
• Establish continuous monitoring of model and rule performance in production
• Develop the champion-challenger framework to keep the best-performing model live
• Participate in infrastructure development for ML model operationalization and inter-service interactions
• Deploy, serve, monitor, and improve production ML systems on a small team

• Develop full-stack machine learning models, including binary classification and regression, for financial-crime detection during transaction processing and client onboarding
• Gather requirements, prototype solutions, and deploy models into production
• Own and evolve the Dynamic Risk Score across all markets, scoring the full client base daily
• Manage transaction rules with ML-driven detection and optimize performance and generated alerts
• Monitor model and rule performance in production
• Develop a champion-challenger framework to keep the best-performing model live
• Participate in infrastructure development for ML operationalization and inter-service interactions
• Serve and infer models of varying complexity

• Develop full-stack machine learning models, including binary classification and regression, for financial crime detection and prevention during transaction processing and client onboarding
• Gather requirements, prototype solutions, and deploy models in production
• Own and evolve the Dynamic Risk Score across all markets
• Score the full client base daily to support decision-making across the client lifecycle
• Manage transaction rules with ML-driven detection and optimize their performance and alert volume
• Establish continuous monitoring of model and rule performance in production
• Develop a champion-challenger framework to keep the best-performing model live
• Contribute to infrastructure development for ML model operationalization and inter-service interactions
• Deploy, serve, monitor, and improve the production ML product

Editor

• Own the words, editorial standard, voice, and final read across the Finom experience
• Build and own the style guide and drive adoption beyond Editorial
• Establish the editorial review layer, criteria, and error-prevention process
• Lead, develop, provide feedback to, and hire copywriting team members
• Own Finom's editorial AI strategy, including usage boundaries and quality gates
• Write personally for key projects, campaigns, and launches
• Coordinate lifecycle, web, paid, and social copy for consistent language
• Partner with Product to maintain brand voice in the product
• Work with localisation partners to improve source-language quality
• Tighten, sharpen, and test copy to improve conversion
• Build intake and prioritisation models for rising demand
• Deliver documented editorial standards, review processes, AI-assisted workflows, and improved conversion and writer performance

Head of Editorial

• Develop full-stack machine learning models, including binary classification and regression, for financial-crime detection and prevention during transaction processing and client onboarding
• Gather requirements, prototype solutions, and deploy models in production
• Own and evolve the Dynamic Risk Score (DRS) across all markets, scoring the full client base daily
• Manage transaction rules with ML-driven detection and optimize performance and generated alerts
• Establish continuous monitoring of model and rule performance in production
• Develop a champion-challenger framework to keep the best-performing model live
• Participate in infrastructure development for ML model operationalization and inter-service interactions
• Serve and infer models of varying complexity
• Deploy, serve, monitor, and improve a production ML product that supports quarantine, alert suppression, and future credit decisions

• Develop full-stack machine learning models, including binary classification and regression, for financial-crime detection and prevention during transaction processing and client onboarding
• Gather requirements, prototype, and deploy models in production
• Own and evolve the Dynamic Risk Score (DRS) across all markets, scoring the full client base daily
• Manage transaction rules with ML-driven detection and optimize performance and alert volumes
• Establish continuous monitoring of model and rule performance in production
• Develop the champion-challenger framework to keep the best-performing model live
• Participate in infrastructure development for ML model operationalization and inter-service interactions
• Deploy, serve, monitor, and improve production models on a small team

• Develop full-stack machine learning models, including binary classification and regression, for financial crime detection and prevention during transaction processing and client onboarding
• Gather requirements, prototype solutions, and deploy models in production
• Own and evolve the Dynamic Risk Score across all markets, scoring the full client base daily
• Manage transaction rules with ML-driven detection and optimize performance and alert volume
• Establish continuous production monitoring for model and rule performance
• Develop a champion-challenger framework to keep the best-performing model live in production
• Participate in infrastructure development for ML model operationalization and inter-service interactions
• Serve and infer models of varying complexity

• Develop full-stack machine learning models, including binary classification and regression, for financial crime detection and prevention during transaction processing and client onboarding
• Gather requirements, prototype solutions, and deploy models in production
• Own and evolve the Dynamic Risk Score (DRS) across all markets
• Score the full client base daily and support decisioning across the client lifecycle
• Manage transaction rules with ML-driven detection and optimize alert performance and volume
• Establish continuous production monitoring for model and rule performance
• Develop the champion-challenger framework to keep the best-performing model live
• Participate in infrastructure development for ML model operationalization and inter-service interactions
• Deploy, serve, monitor, and improve production ML systems on a small team

Manager

• Build and lead the credit risk framework for Finom’s SME Credit Cards/Overdraft products
• Oversee, build, and improve Finom’s Credit Risk engine, including its framework, guidance, analytics, and technical setup
• Own and develop the end-to-end credit risk framework for credit cards, including eligibility, underwriting, scoring models, limit setting, risk appetite, monitoring, and reporting
• Partner with Product and Engineering to embed risk into product design and strategy
• Architect the data strategy using transactional, bureau, behavioural, accounting, government registry, and first-party account data
• Guide the ML & Data Science team in building, validating, and iterating credit scoring and related risk models
• Build and own the portfolio monitoring stack
• Design, build, and calibrate early warning indicators and credit monitoring systems
• Act as a Second Line of Defense, ensuring robust origination, monitoring, and collections processes
• Own and improve credit risk policies, risk appetite frameworks, and reporting
• Ensure alignment with EMI/PS4 regulatory requirements and evolving European standards
• Collaborate with the CRO, Product, ML & Data Science, Operations, and Engineering teams

Credit Risk Manager – SME Lending

Artificial Intelligence

• Own AI implementation for transaction monitoring alert resolution from identifying high-value manual steps through production deployment
• Understand the alert resolution process from pickup through false positive, true positive or offboarding
• Build AI-driven investigations of customers, suspicious transactions, behavioural patterns and network patterns
• Build and iterate AI agents that replace manual analyst work with boundaries, escalation logic and audit trails
• Redesign alert resolution around automation, validation and human decision-making
• Define and document the AI/AML boundary for risk management, audit and regulators
• Support and upskill analysts during the transition to model and output validation
• Analyse alert and customer data to identify inefficiencies, false-positive drivers and automation opportunities
• Measure process changes and their impact
• Partner with AI, Data, Engineering, Product and risk-management stakeholders
• Ensure efficiency improvements do not degrade customer experience
• Own audit-standard procedures, decision logic, model rationale and controls
• Drive reductions in alert handling time and backlog
• Increase AI-resolved or pre-processed alerts while maintaining or improving quality
• Track QA, validation sampling, audit outcomes, analyst capacity and customer-facing impact

AI Process Owner – Transaction Monitoring, Customer Lifecycle

• Own AI implementation for transaction monitoring alert resolution from identifying high-value manual steps through production deployment
• Understand the alert resolution process from pickup to false positive, true positive, or offboarding
• Build AI-driven investigation capabilities for customers, suspicious transactions, and behavioural and network patterns
• Build and iterate AI agents that replace manual analyst work with clear boundaries, escalation logic, and audit trails
• Redesign alert resolution around automation, validation, human review, and quality evidence
• Define and document the AI/AML boundary for risk management, audit, and regulators
• Support and upskill analysts transitioning to model and output validation
• Analyse alert and customer data to identify inefficiencies, false-positive drivers, and automation opportunities
• Partner with AI, Data, Engineering, Product, and risk management teams to translate operational and AML needs into technical requirements
• Improve customer experience by reducing unnecessary touchpoints and resolution delays
• Own procedures, decision logic, model rationale, and controls to audit standards
• Track cycle time, alert backlog, AI resolution share, decision quality, analyst capacity, and customer-facing impact

• Own AI implementation for transaction monitoring alert resolution from identifying manual steps through production deployment
• Understand alert resolution from pickup through false positive, true positive or offboarding
• Build AI-driven investigations of customers, suspicious transactions, behavioural patterns and network patterns
• Build and iterate AI agents with boundaries, escalation logic and audit trails
• Redesign alert resolution around automation, validation and human decision-making
• Define and document the AI/AML boundary for risk management, audit and regulators
• Support and upskill analysts transitioning to model and output validation
• Analyse alert and customer data to identify inefficiencies, false-positive drivers and automation opportunities
• Measure cycle time, alert backlog, AI resolution share, decision quality, analyst capacity and customer impact
• Partner with AI, Data, Engineering, Product and risk management stakeholders
• Maintain customer-focused processes that improve efficiency without degrading customer experience
• Own procedures, decision logic, model rationale and controls to audit standard

• Build and run Finom's treasury function
• Manage daily liquidity positioning and float across entities and accounts
• Fund payment channels intraday and manage nostro and correspondent accounts
• Manage collateral and minimum-balance obligations across card schemes, T2, and credit providers
• Develop and maintain contingency funding plans
• Produce a reconciled daily treasury balance sheet showing net liquidity and net capital
• Manage safeguarded client-fund portfolios, including term deposits, MMFs, and short-dated debt
• Detect safeguarding shortfalls, execute top-ups, and maintain same-currency discipline
• Screen instruments under CRR, UCITS, and MMF constraints
• Validate externally managed allocations through position-level look-through and monitor breaches
• Forecast and attribute NII and provide treasury inputs to financial models and company forecasts
• Own investment policy, limit framework, eligible-instrument register, and treasury governance
• Conduct pre-deal checks, post-trade evidencing, covenant tracking, and regulator, audit, and second-line engagement
• Select and implement investment accounting and treasury tooling
• Integrate custodian, asset-manager, and bank data feeds and replace manual spreadsheet processes
• Manage bank, custodian, broker, and asset-manager relationships, including onboarding, terms, mandates, and service reviews
• Challenge managers on allocation, transparency, and fees
• Deliver a live investment policy and limit framework, documented decision log, evidenced mandate compliance, implemented investment accounting platform, reconciled NII reporting, and tested contingency liquidity playbook within the first 12 months

Art Director, Brand & Marketing Design

Senior Machine Learning Engineer

Senior Machine Learning Engineer

Senior Machine Learning Engineer

Senior Machine Learning Engineer

Head of Editorial

Senior Machine Learning Engineer

Senior Machine Learning Engineer

Senior Machine Learning Engineer

Senior Machine Learning Engineer

Credit Risk Manager – SME Lending

AI Process Owner – Transaction Monitoring, Customer Lifecycle

AI Process Owner – Transaction Monitoring, Customer Lifecycle

AI Process Owner – Transaction Monitoring, Customer Lifecycle

Senior Treasury Manager