Physical Climate Risk Model Developer, Vice President

The core mission is to convert physical climate and nature-related hazards into quantified financial and credit risk. That means developing hazard, exposure, vulnerability and impact models, building geospatial pipelines over complex climate and asset-level datasets, and translating scientific drivers into metrics usable for stress testing, scenario analysis and portfolio analytics. The role also integrates external vendor and public hazard models with Citi's internal risk and portfolio data. Beyond modelling itself, the position carries a strong governance dimension. You would perform sensitivity analysis, benchmarking and model testing, write production-grade analytical code, and author the technical documentation that supports Model Risk Management validation and regulatory review. Expect to present findings to both technical and non-technical audiences. Longer term, the objective is to embed credible physical risk quantification into how Citi identifies, measures and reports climate risk across portfolios, sectors and geographies, making the bank's climate risk framework defensible to regulators and useful to the business.

This role sits at a genuinely rare intersection: catastrophe modelling, climate science and bank risk management. A candidate would likely deepen expertise in industry CAT modelling methodologies, vendor and public weather and hazard models, and the regulatory expectations shaping climate risk disclosure in a global systemic bank. On the technical side, expect hands-on growth in geospatial analytics, Bayesian inference, spatial statistics and machine learning applied to very large, messy environmental and asset-level datasets. Writing production-grade code inside a controlled technology environment is a distinct skill from research prototyping, and this position builds it. The governance exposure is equally valuable. Preparing methodologies for MRM validation and regulatory requirements gives a modeller the credibility to move into model risk, climate risk policy, or broader enterprise risk leadership later.

The posting is explicit that an advanced degree, MS or PhD, in a quantitative natural-hazard discipline is required: hydrology, meteorology, climate science, environmental or life sciences, statistics, physics, engineering or mathematics. Alongside that, six to ten years of applied experience in quantitative modelling, data science, climate risk, CAT modelling or environmental risk analytics sets the bar. Technically, the ideal profile combines statistical modelling or machine learning with strong Python, SQL and Unix/Linux proficiency, plus demonstrated experience with hazard, exposure and vulnerability data. Familiarity with vendor catastrophe models and climate data sources matters more here than general banking experience. Softer skills are non-trivial. The role partners with vendors, first- and second-line teams and business sponsors, so the ability to translate model design, assumptions and limitations clearly, and to take credible challenge gracefully, is as important as the modelling itself.

Because the required qualification is unusually specific (an advanced degree in a natural-hazard-adjacent discipline plus 6–10+ years in CAT, climate or environmental risk modelling) the resume should lead with that intersection rather than a generic data science narrative. Name the hazards and models you have worked with, the datasets, and whether your work fed stress testing or regulatory reporting. Given Citi's emphasis on model governance, evidence of documentation authored for validation committees or regulators is a strong differentiator. Concrete examples of translating physical hazard outputs into financial or credit impact metrics, and of deploying models into controlled production environments, speak directly to the job description. In interviews, prepare to explain a model you built end to end: assumptions, sensitivity, benchmark choices and limitations. Citi explicitly values credible challenge and clear communication to non-technical audiences, so rehearse a plain-language version of your most technical work.

The sustainability angle here is climate risk quantification rather than emissions reduction. This role focuses squarely on the physical side of climate risk (acute hazards like floods and storms, chronic shifts like heat and sea-level change) and increasingly on nature-related risk, feeding Citi's scenario analysis and stress testing. The work underpins how a global systemic bank understands the financial transmission of environmental change: which assets, sectors and geographies are exposed, how vulnerable they are, and what that means for credit and portfolio stability. That makes it central to regulatory climate risk obligations. More broadly, Citi has committed to net-zero alignment and sustainable finance targets, but this seat's contribution is the analytical backbone, credible, governed, quantitative evidence that lets the bank price and manage environmental risk rather than simply report on it.

The hardest problem is data. Physical hazard, exposure and vulnerability datasets are patchy, inconsistently scaled and often vendor-controlled, so a modeller here will spend real energy on sourcing, reconciling and validating inputs before any modelling begins. Geospatial alignment across portfolios and geographies adds further complexity. A second challenge is translation. Scientific and hazard-model outputs rarely map cleanly onto financial metrics, and reconciling time horizons, return periods and scenario assumptions with credit risk frameworks requires judgment that will be scrutinised by Model Risk Management. Finally, there is the governance tension: models must be innovative enough to capture emerging climate and nature risk yet stable, documented and defensible enough to pass validation and satisfy regulators, a balance that likely defines much of the day-to-day pressure.

A typical day would mix deep technical work with coordination. Expect blocks of time writing and debugging Python and SQL pipelines, processing geospatial hazard layers or integrating vendor datasets with internal portfolio data, and running sensitivity or benchmarking tests on models under development. Much of the calendar, however, is collaborative. The role partners with external vendors on data sourcing and with first- and second-line teams on designing climate risk assessment features, so calls with technology, model risk and business sponsors are routine. Documentation for governance and validation also claims consistent time. Variability comes with the cycle: quieter periods favour model development and research, while stress-testing exercises, regulatory submissions or validation reviews push the balance toward testing, write-ups and presenting findings to technical and non-technical audiences.

The visibility is a major asset. Because physical climate risk modelling feeds stress testing, scenario analysis and regulatory reporting at a global systemic bank, strong work here is seen well beyond the immediate team, by enterprise risk leadership, Model Risk Management and regulators. From this seat, natural progressions include deeper specialisation within Climate Risk Analytics, movement into broader enterprise risk or model risk leadership, or lateral shifts toward sustainable finance and climate strategy functions. The combination of quantitative depth and governance exposure is portable across banking, insurance and advisory. Citi also frames the role within a large Data, Analytics, Reporting and Technology organisation, which means exposure to data engineering, technology deployment and cross-functional programme work that can widen a modeller's career options considerably.

Internally, the closest relationships are within the Climate Risk Analytics team inside Credit, Climate and Obligor Risk Analytics, plus colleagues across the Data, Analytics, Reporting and Technology organisation. Technologists are constant partners, since models must be implemented in controlled production environments. Beyond the immediate team, the role interacts with the First Line of Defence on requirement-setting and data sourcing, and with Second Line functions including Model Risk Management on validation and governance. Business sponsors and portfolio analysts consume the outputs, so presenting to non-technical audiences is a regular duty. Externally, climate data and catastrophe model vendors are key counterparts, as the role evaluates and integrates their models and collaborates on hazard, exposure and vulnerability data. Regulators are a less frequent but high-stakes audience, reached indirectly through documentation and reporting.