<div class="t396__elem tn-elem tn-elem__7327515811698693224706" data-elem-id="1698693224706" data-elem-type="text" data-field-top-value="430" data-field-left-value="271" data-field-height-value="312" data-field-width-value="634" data-field-axisy-value="top" data-field-axisx-value="left" data-field-container-value="grid" data-field-topunits-value="px" data-field-leftunits-value="px" data-field-heightunits-value="px" data-field-widthunits-value="px" data-field-textfit-value="autoheight" data-field-top-res-320-value="651" data-field-left-res-320-value="10" data-field-width-res-320-value="306" data-field-top-res-480-value="622" data-field-left-res-480-value="80" data-field-width-res-480-value="333" data-field-top-res-640-value="480" data-field-left-res-640-value="60" data-field-width-res-640-value="487" data-field-top-res-960-value="430" data-field-left-res-960-value="152" data-field-width-res-960-value="582" data-fields="top,left,width,height,container,axisx,axisy,widthunits,leftunits,topunits"> <div class="tn-atom"> <div class="t396__elem tn-elem tn-elem__7327515811698693278131" data-elem-id="1698693278131" data-elem-type="text" data-field-top-value="836" data-field-left-value="271" data-field-height-value="156" data-field-width-value="598" data-field-axisy-value="top" data-field-axisx-value="left" data-field-container-value="grid" data-field-topunits-value="px" data-field-leftunits-value="px" data-field-heightunits-value="px" data-field-widthunits-value="px" data-field-textfit-value="autoheight" data-field-top-res-320-value="1384" data-field-left-res-320-value="10" data-field-height-res-320-value="234" data-field-width-res-320-value="306" data-field-container-res-320-value="grid" data-field-top-res-480-value="1302" data-field-left-res-480-value="80" data-field-width-res-480-value="324" data-field-top-res-640-value="937" data-field-left-res-640-value="60" data-field-width-res-640-value="485" data-field-top-res-960-value="862" data-field-left-res-960-value="152" data-fields="top,left,width,height,container,axisx,axisy,widthunits,leftunits,topunits"> <div class="tn-atom"> <div class="t396__elem tn-elem tn-elem__8559900061715530713485" data-elem-id="1715530713485" data-elem-type="text" data-field-top-value="1024" data-field-left-value="271" data-field-height-value="20" data-field-width-value="656" data-field-axisy-value="top" data-field-axisx-value="left" data-field-container-value="grid" data-field-topunits-value="px" data-field-leftunits-value="px" data-field-heightunits-value="px" data-field-widthunits-value="px" data-field-textfit-value="autoheight" data-field-top-res-320-value="1715" data-field-left-res-320-value="12" data-field-width-res-320-value="195" data-field-container-res-320-value="grid" data-field-top-res-480-value="1638" data-field-left-res-480-value="80" data-field-width-res-480-value="318" data-field-top-res-640-value="1204" data-field-left-res-640-value="65" data-field-width-res-640-value="307" data-field-top-res-960-value="1051" data-field-left-res-960-value="153" data-fields="top,left,width,height,container,axisx,axisy,widthunits,leftunits,topunits"> <div class="tn-atom"> <h3><strong>About the role</strong></h3> <p class="mb-xs">We currently have several <strong>Analyst</strong> openings across our Risk teams in Mexico and Colombia, specifically within Retail Credit Risk.</p> <p class="mb-xs">You will join one of these teams and use data to solve real business problems, improve risk strategies and processes, and support better decision-making across our credit products.</p> <p class="mb-xs">Previous risk experience is not required — we value strong mathematical and analytical thinking, solid SQL and Python skills, and the ability to quickly understand and solve complex problems</p> <p> </p> <h3><strong>Challenges that await you:</strong></h3> <ul> <li>Analyze credit funnels, portfolio performance and key risk indicators to identify trends, risks, friction points, and opportunities</li> <li>Develop and maintain analytical datasets, reports, dashboards, and BI solutions to support business and risk decision-making</li> <li>Help design and optimize credit decision strategies, evaluating their impact on approval rates, portfolio quality, recovery, and customer behavior</li> <li>Analyze the profitability, NPV, and unit economics of credit products and identify the key parameters driving their performance</li> <li>Prototype, evaluate, and monitor analytical and machine learning models, including testing hypotheses and assessing model performance</li> <li>Work closely with Risk, Product, Data, Data Engineering, and business teams to turn data analysis into practical decisions, tests, and new processes</li> </ul> <h3><strong>What makes you a great fit:</strong></h3> <ul> <li><strong>Completed education</strong>: Mathematics, physics, engineering, economics, computer science, or another strong quantitative field</li> <li>Strong knowledge of SQL and Python; experience with data analysis libraries and machine learning tools is a plus</li> <li>Good mathematical intuition and a solid understanding of probability and statistics</li> <li>Ability to solve unfamiliar, non-trivial problems and quickly find logical, data-driven solutions</li> <li>Analytical mindset: curiosity, attention to detail, willingness to learn, and the ability to clearly explain your conclusions</li> <li>B1 or higher English level for effective communication with an international team</li> </ul> <h3><strong>Nice to have:</strong></h3> <ul> <li>Up to 2 years of experience in analytics</li> <li>Experience in risk analytics<