Position Summary Vana is building its Credit Card Risk Analytics function from the ground up, and this role is its founding hire. The Credit Card Risk Analytics Lead will own the end-to-end design, implementation, monitoring, and optimization of credit Card risk strategy for the card portfolio: digital underwriting, initial limit assignment, behavioral line management, portfolio quality, and risk-adjusted profitability. This is a hands-on, high-autonomy build role. In the first year, the Lead personally establishes the data foundations, the decisioning framework, the experimentation discipline, and the governance standards of the area — and then shapes and hires the analytics team that will grow around it as the portfolio scales across markets. The right candidate combines deep technical craft in cards/revolving risk with the ownership mentality of someone building a function, not inheriting one. Reporting & Location
• Reports to: Head of Risk Vana.
• Direct reports: None initially; the role carries an explicit mandate to design and hire the Credit Card Risk Analytics team as the portfolio grows.
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Location:
Remote within Latin America. Guatemala-based is welcome but not required; occasional travel to Guatemala City (HQ) for planning cycles and committee sessions.
• Key interfaces: Product, Engineering, Data Science, Fraud Prevention, Collections, Finance, and Compliance; presents to the Risk Committee. Mandate: Build the Credit Card Risk Analytics Function Beyond running day-to-day analytics, this role carries a function-building mandate with three phases:
• Foundation (months 1–6): Stand up the card risk data architecture — vintage, roll-rate, utilization, exposure, and loss datasets with clear definitions; a single source of truth for portfolio reporting; and the core risk dashboard consumed by the Head of Risk, Finance, and the Risk Committee.
• Decisioning (months 4–12): Establish the decisioning framework — underwriting strategy, segmentation, initial-limit logic, and limit-management rules deployed through the decision engine with Engineering, under a champion/challenger testing discipline.
• Team & scale (months 9+): Define the operating model and roles of the area (credit strategy analysts, monitoring/reporting, model analytics), build the hiring plan, recruit, and develop the team; codify standards so the function scales with the portfolio and across countries.

Key Responsibilities
Underwriting & Origination Strategy
• Design and own the digital underwriting strategy: eligibility criteria, risk segmentation, score cutoffs, and knockout rules across bureau, alternative, and internal group data (including performance from other Vana products).
• Define initial credit limit assignment logic by segment, balancing activation, affordability, and loss expectations; set exposure caps and concentration guardrails.
• Evaluate affordability and payment capacity (ability-to-pay) at origination and at every material exposure change.
• Calibrate approval rates against early-default performance; manage the approval/credit-quality trade-off explicitly against approved risk appetite. Credit Line Management — Progressive Limit Framework
• Own the design and governance of the progressive limit framework:



repayment replenishes available credit but never raises the authorized limit automatically. Every line increase requires evidence of repayment reliability, payment capacity, healthy utilization behavior, risk stability, and acceptable incremental economics.
• Build behavior-based credit line increase (CLI), line freeze, and line decrease strategies, including eligibility rules, increase caps and pacing, exclusions (e.g., persistent revolvers, recent delinquency, over-limit behavior), and cooling-off periods.
• Quantify incremental exposure, incremental loss, and incremental revenue of each limit action; approve only actions with positive risk-adjusted economics.
• Design post-increase monitoring: matched-cohort comparison of increased vs. non-increased accounts on delinquency, utilization, and revolving debt accumulation. Portfolio Monitoring & Analytics
• Build and maintain the card portfolio monitoring suite: vintage curves, roll rates, flow-to-loss, 30+/60+/90+ DPD, utilization and revolve/transact mix, payment rates, over-limit incidence, and net credit losses.
• Produce monthly portfolio quality reviews for the Head of Risk and the Risk Committee, with early-warning triggers and recommended actions.
• Evaluate cohort-level unit economics on a risk-adjusted basis: revenue, cost of risk, and risk-adjusted margin by vintage, segment, channel, and country.
• Run deep dives on emerging deterioration (segment, geography, acquisition channel) and translate findings into policy or strategy changes. Modeling & Decision Science
• Develop, validate, and maintain application and behavioral scorecards, and PD/LGD/EAD analytics for the revolving portfolio, including inputs to IFRS 9 expected-loss estimation in coordination with Finance.
• Build early-warning models (payment stress, utilization spikes, cash-advance patterns, bureau alerts) feeding line-management and pre-delinquency treatment.
• Monitor model performance: discrimination (Gini/KS), calibration, stability (PSI), and drift; define recalibration and redevelopment triggers.
• Document every model and strategy to an auditable standard: data lineage, assumptions, validation results, limitations, and approval trail. Decision Engine & Experimentation
• Translate strategies into production decision rules with Engineering; own rule specifications, test cases, and pre/post-deployment validation.
• Establish the champion/challenger and A/B testing discipline for underwriting and line-management changes: test design, sample sizing, success criteria, and read-out cadence.
• Drive progressive automation of credit decisions while keeping manual-review queues well-defined and measured. Cross-Functional Integration
• Integrate fraud signals into credit strategy with the Fraud team; ensure credit and fraud rules are coherent and jointly optimized.
• Feed collections with risk segmentation for treatment intensity,



and incorporate collections outcomes (cure rates, recoveries) back into loss forecasting and strategy design.
• Partner with Finance on loss forecasting, provisioning inputs, and portfolio planning; with Product on how risk constraints shape customer experience; and with Compliance on customer-protection requirements per market. Governance & Documentation
• Maintain the credit strategy and model inventory of Vana Card; ensure every material change is documented, tested, approved under delegated authorities, and monitored post-implementation.
• Prepare and defend strategy proposals before the Risk Committee. Candidate Profile Experience
• 5–7+ years in consumer credit risk analytics within fintech, credit cards, or retail banking, including 3+ years directly in underwriting strategy, behavioral scoring, or portfolio risk management.
• Hands-on experience with revolving credit / credit card portfolios is strongly preferred: limit management, utilization dynamics, revolve/transact behavior, and card unit economics.
• Track record of taking credit strategies from analysis to production: rules deployed in a decision engine, tested, and measured — not only analyzed.
• Experience in emerging-market or LATAM consumer lending (thin-file borrowers, alternative data, bureau limitations) is a strong plus.
• Experience building or scaling an analytics function, or being the first/senior analytics hire in a growing portfolio, is highly valued given the build mandate of this role. Technical Skills
• Advanced SQL and strong Python for analysis and modeling (pandas, scikit-learn or equivalent); comfort working directly with raw transactional and bureau data.
• Solid applied statistics: logistic regression and tree-based models, validation methodology, sampling, and experiment design.
• Mastery of card-risk analytical toolkit: vintage analysis, roll rates, flow-to-loss, migration matrices, cohort economics.
• Experience configuring or specifying rules in credit decision engines; familiarity with BI tools (Metabase, Looker, Power BI, or similar).
• Working knowledge of IFRS 9 expected-loss concepts as applied to revolving portfolios is a plus. ( specifically for revolving products like credit cards, which behave differently from installment loans ) Leadership & Ways of Working
• Builder profile: comfortable with ambiguity, missing infrastructure, and imperfect data; creates structure rather than waiting for it.
• Strong ownership and intellectual honesty: presents what the data shows, including when it argues against growth.
• Executive communication: translates analytics into clear credit-policy recommendations and defends them before committees and senior leadership.
• Collaborative operator across Product, Engineering, and Finance; able to write specifications engineers can build from.
• Prepared to hire, coach, and develop analysts as the function grows. Education & Languages
• Bachelor's degree in Statistics, Economics, Finance, Mathematics, Engineering, Computer Science, or a related quantitative discipline; a relevant master's degree is preferred.
• Professional working proficiency in Spanish and English (documentation and committee work run in both).

📌 Credit card Risk Analytics Lead (Argentina)
🏢 Vana
📍 Argentina