[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"seo-content-expertise-survival-analysis-global":3,"job-search-\u002Fde-global\u002Fjobs-by-expertise\u002Fsurvival-analysis":4,"expertise-filters-\u002Fde-global\u002Fjobs-by-expertise\u002Fsurvival-analysis":113,"featured-jobs-\u002Fde-global\u002Fjobs-by-expertise\u002Fsurvival-analysis":217},{},{"total":5,"jobs":6},2,[7,68],{"id":8,"slug":9,"title":10,"raw_title":11,"is_featured":12,"company":13,"raw_hiringOrganization_logo_url":11,"processed_city_gmaps":17,"processed_country_iso_code_gmaps":18,"processed_home_office":20,"processed_salary_min":21,"processed_salary_max":22,"processed_salary_currency":23,"processed_salary_source":24,"processed_working_hours":25,"processed_employment_types":27,"processed_it_skills":28,"processed_it_skills_labels":31,"processed_job_expertise_skills":41,"processed_job_expertise_skills_labels":42,"max_cpc":67,"actual_cpc":67},87130,"data-analytics-sr-leader-banco-santander-brasil-sa","Data & Analytics Sr Leader",null,false,{"name":14,"slug":15,"logo_url":16},"Banco Santander (Brasil) S.A.","banco-santander-brasil-sa","\u002Fimages\u002Flogos\u002Fsantander-logo-2018.svg",[],[19],"BR","onsite",240000,360000,"BRL","estimated",[26],"FULL_TIME","permanent",{"git":29,"sql":30,"numpy":30,"spark":30,"pandas":30,"python":30,"pyspark":30,"databricks":30,"scikit-learn":30},3,4,[32,33,34,35,36,37,38,39,40],"Python","SQL","Spark","PySpark","Databricks","Git","Pandas","NumPy","Scikit-Learn",{"gam":30,"glm":30,"ifrs 9":30,"pd modeling":30,"ead modeling":30,"lgd modeling":30,"stress testing":30,"machine learning":30,"model monitoring":30,"model validation":30,"stage allocation":30,"survival analysis":30,"people development":30,"project management":30,"agile methodologies":30,"logistic regression":30,"regulatory modeling":30,"resolução cmn 4.966":30,"credit risk management":30,"expected credit loss (ecl)":30,"generative ai (genai and llms)":30,"audit and regulatory compliance":30,"backtesting and model calibration":30,"applied statistics for risk modeling":30},[43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66],"IFRS 9","Resolução CMN 4.966","PD Modeling","LGD Modeling","EAD Modeling","Expected Credit Loss (ECL)","Stage Allocation","Stress Testing","Applied Statistics for Risk Modeling","Logistic Regression","Survival Analysis","GLM","GAM","Backtesting and Model Calibration","Machine Learning","Generative AI (GenAI and LLMs)","Credit Risk Management","Regulatory Modeling","Model Validation","Model Monitoring","Audit and Regulatory Compliance","Agile Methodologies","Project Management","People Development",1,{"id":69,"slug":70,"title":71,"raw_title":11,"is_featured":12,"company":72,"raw_hiringOrganization_logo_url":11,"processed_city_gmaps":75,"processed_country_iso_code_gmaps":77,"processed_home_office":20,"processed_salary_min":79,"processed_salary_max":80,"processed_salary_currency":81,"processed_salary_source":24,"processed_working_hours":82,"processed_employment_types":27,"processed_it_skills":83,"processed_it_skills_labels":84,"processed_job_expertise_skills":90,"processed_job_expertise_skills_labels":91,"max_cpc":67,"actual_cpc":112},72875,"lead-data-scientist-ecl-modelling-gx-bank-berhad","Lead Data Scientist (ECL Modelling)",{"name":73,"slug":74,"logo_url":11},"GX Bank Berhad","gx-bank-berhad",[76],"Petaling Jaya",[78],"MY",180000,300000,"MYR",[26],{"aws":29,"git":29,"sql":30,"ci\u002Fcd":29,"numpy":30,"pandas":30,"python":30,"statsmodels":30,"scikit-learn":30},[32,33,85,39,86,87,37,88,89],"pandas","scikit-learn","statsmodels","CI\u002FCD","AWS",{"irb models":29,"stress testing":30,"machine learning":29,"model deployment":29,"model governance":30,"model validation":30,"recovery analysis":30,"survival analysis":30,"capital management":29,"logistic regression":30,"portfolio analytics":30,"transition matrices":30,"ifrs 9 ecl modelling":30,"mfrs 9 ecl modelling":30,"model implementation":30,"time-series analysis":30,"credit risk modelling":30,"regulatory compliance":30,"statistical modelling":30,"credit risk scorecards":30,"macroeconomic modelling":30,"basel credit risk models":29,"loss given default (lgd) modelling":30,"exposure at default (ead) modelling":30,"probability of default (pd) modelling":30},[92,93,94,95,96,97,98,99,100,61,101,102,103,57,52,53,104,105,106,107,50,108,109,110,111],"Credit Risk Modelling","MFRS 9 ECL Modelling","IFRS 9 ECL Modelling","Probability of Default (PD) Modelling","Loss Given Default (LGD) Modelling","Exposure at Default (EAD) Modelling","Credit Risk Scorecards","Portfolio Analytics","Model Implementation","Model Governance","Regulatory Compliance","Statistical Modelling","Transition Matrices","Time-Series Analysis","Macroeconomic Modelling","Recovery Analysis","Capital Management","Basel Credit Risk Models","IRB Models","Model Deployment",0.5,{"industry":114,"employment_type":121,"expertise_skills":124,"it_skills":185,"salary_currency":212,"processed_working_hours":215},[115,118],{"key":116,"label":117,"count":67},"banking","Banking",{"key":119,"label":120,"count":67},"digital banking","Digital Banking",[122],{"key":27,"label":123,"count":5},"PERMANENT",[125,127,129,131,133,135,137,139,141,143,146,149,152,155,158,161,164,167,170,173,176,178,181,183],{"key":126,"label":57,"slug":126,"count":5},"machine-learning",{"key":128,"label":50,"slug":128,"count":5},"stress-testing",{"key":130,"label":53,"slug":130,"count":5},"survival-analysis",{"key":132,"label":61,"slug":132,"count":5},"model-validation",{"key":134,"label":52,"slug":134,"count":5},"logistic-regression",{"key":136,"label":108,"slug":136,"count":67},"capital-management",{"key":138,"label":59,"slug":138,"count":67},"credit-risk-management",{"key":140,"label":92,"slug":140,"count":67},"credit-risk-modelling",{"key":142,"label":98,"slug":142,"count":67},"credit-risk-scorecards",{"key":144,"label":145,"slug":144,"count":67},"ead-modeling","Ead Modeling",{"key":147,"label":148,"slug":147,"count":67},"expected-credit-loss-ecl","Expected Credit Loss (Ecl)",{"key":150,"label":151,"slug":150,"count":67},"exposure-at-default-ead-modelling","Exposure At Default (Ead) Modelling",{"key":153,"label":154,"slug":153,"count":67},"gam","Gam",{"key":156,"label":157,"slug":156,"count":67},"generative-ai-genai-and-llms","Generative Ai (Genai And Llms)",{"key":159,"label":160,"slug":159,"count":67},"glm","Glm",{"key":162,"label":163,"slug":162,"count":67},"ifrs-9","Ifrs 9",{"key":165,"label":166,"slug":165,"count":67},"ifrs-9-ecl-modelling","Ifrs 9 Ecl Modelling",{"key":168,"label":169,"slug":168,"count":67},"irb-models","Irb Models",{"key":171,"label":172,"slug":171,"count":67},"lgd-modeling","Lgd Modeling",{"key":174,"label":175,"slug":174,"count":67},"loss-given-default-lgd-modelling","Loss Given Default (Lgd) Modelling",{"key":177,"label":106,"slug":177,"count":67},"macroeconomic-modelling",{"key":179,"label":180,"slug":179,"count":67},"mfrs-9-ecl-modelling","Mfrs 9 Ecl Modelling",{"key":182,"label":111,"slug":182,"count":67},"model-deployment",{"key":184,"label":101,"slug":184,"count":67},"model-governance",[186,188,190,191,194,196,197,199,202,204,207,209],{"key":187,"label":37,"slug":187,"count":5},"git",{"key":189,"label":32,"slug":189,"count":5},"python",{"key":86,"label":40,"slug":86,"count":5},{"key":192,"label":193,"slug":192,"count":5},"numpy","Numpy",{"key":195,"label":33,"slug":195,"count":5},"sql",{"key":85,"label":38,"slug":85,"count":5},{"key":87,"label":198,"slug":87,"count":67},"Statsmodels",{"key":200,"label":201,"slug":200,"count":67},"cicd","Ci\u002FCd",{"key":203,"label":36,"slug":203,"count":67},"databricks",{"key":205,"label":206,"slug":205,"count":67},"pyspark","Pyspark",{"key":208,"label":34,"slug":208,"count":67},"spark",{"key":210,"label":211,"slug":210,"count":67},"aws","Aws",[213,214],{"key":23,"label":23,"count":67},{"key":81,"label":81,"count":67},[216],{"key":26,"label":26,"count":5},{"total":67,"jobs":218},[219],{"id":220,"slug":221,"title":222,"raw_title":11,"is_featured":223,"company":224,"raw_hiringOrganization_logo_url":11,"processed_city_gmaps":228,"processed_country_iso_code_gmaps":230,"processed_home_office":232,"processed_salary_min":233,"processed_salary_max":234,"processed_salary_currency":235,"processed_salary_source":11,"processed_working_hours":236,"processed_employment_types":123,"processed_it_skills":238,"processed_it_skills_labels":11,"processed_job_expertise_skills":239,"processed_job_expertise_skills_labels":240,"max_cpc":246,"actual_cpc":11},52265,"privatkundenbetreuer-mwd-banking-frankfurt-am-main-white-horse-international-gmbh","Privatkundenbetreuer (m\u002Fw\u002Fd) – Banking | Frankfurt am Main",true,{"name":225,"slug":226,"logo_url":227},"White Horse International GmbH","white-horse-international-gmbh","https:\u002F\u002Fwhitehorse-international.com\u002Fwp-content\u002Fuploads\u002F2022\u002F03\u002Fwhite-horse-logo-final-gif.gif",[229],"Frankfurt am Main",[231],"DE","HYBRID",45000,65000,"EUR",[237],"full_time",{},{"Financial Analysis":29,"Regulatory Compliance":29,"New Customer Acquisition":29,"Private Banking Advisory":29,"Banking Products Knowledge":29,"Customer Relationship Management":29},[241,242,243,102,244,245],"Private Banking Advisory","Financial Analysis","Banking Products Knowledge","Customer Relationship Management","New Customer Acquisition",0.8]