[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"seo-content-expertise-transition-matrices-my":3,"job-search-\u002Fen-my\u002Fjobs-by-expertise\u002Ftransition-matrices":4,"expertise-filters-\u002Fen-my\u002Fjobs-by-expertise\u002Ftransition-matrices":69,"featured-jobs-\u002Fen-my\u002Fjobs-by-expertise\u002Ftransition-matrices":158},{},{"total":5,"jobs":6},1,[7],{"id":8,"slug":9,"title":10,"raw_title":11,"is_featured":12,"company":13,"raw_hiringOrganization_logo_url":11,"processed_city_gmaps":16,"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":5,"actual_cpc":68},72875,"lead-data-scientist-ecl-modelling-gx-bank-berhad","Lead Data Scientist (ECL Modelling)",null,false,{"name":14,"slug":15,"logo_url":11},"GX Bank Berhad","gx-bank-berhad",[17],"Petaling Jaya",[19],"MY","onsite",180000,300000,"MYR","estimated",[26],"FULL_TIME","permanent",{"aws":29,"git":29,"sql":30,"ci\u002Fcd":29,"numpy":30,"pandas":30,"python":30,"statsmodels":30,"scikit-learn":30},3,4,[32,33,34,35,36,37,38,39,40],"Python","SQL","pandas","NumPy","scikit-learn","statsmodels","Git","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},[43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67],"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 Validation","Model Governance","Regulatory Compliance","Statistical Modelling","Machine Learning","Logistic Regression","Survival Analysis","Transition Matrices","Time-Series Analysis","Macroeconomic Modelling","Recovery Analysis","Stress Testing","Capital Management","Basel Credit Risk Models","IRB Models","Model Deployment",0.4,{"industry":70,"employment_type":74,"expertise_skills":77,"it_skills":132,"salary_currency":154,"processed_working_hours":156},[71],{"key":72,"label":73,"count":5},"digital banking","Digital Banking",[75],{"key":27,"label":76,"count":5},"PERMANENT",[78,80,82,84,86,89,92,95,97,100,102,104,107,109,111,113,115,117,120,122,124,126,128,130],{"key":79,"label":65,"slug":79,"count":5},"basel-credit-risk-models",{"key":81,"label":64,"slug":81,"count":5},"capital-management",{"key":83,"label":43,"slug":83,"count":5},"credit-risk-modelling",{"key":85,"label":49,"slug":85,"count":5},"credit-risk-scorecards",{"key":87,"label":88,"slug":87,"count":5},"exposure-at-default-ead-modelling","Exposure At Default (Ead) Modelling",{"key":90,"label":91,"slug":90,"count":5},"ifrs-9-ecl-modelling","Ifrs 9 Ecl Modelling",{"key":93,"label":94,"slug":93,"count":5},"irb-models","Irb Models",{"key":96,"label":57,"slug":96,"count":5},"logistic-regression",{"key":98,"label":99,"slug":98,"count":5},"loss-given-default-lgd-modelling","Loss Given Default (Lgd) Modelling",{"key":101,"label":56,"slug":101,"count":5},"machine-learning",{"key":103,"label":61,"slug":103,"count":5},"macroeconomic-modelling",{"key":105,"label":106,"slug":105,"count":5},"mfrs-9-ecl-modelling","Mfrs 9 Ecl Modelling",{"key":108,"label":67,"slug":108,"count":5},"model-deployment",{"key":110,"label":53,"slug":110,"count":5},"model-governance",{"key":112,"label":51,"slug":112,"count":5},"model-implementation",{"key":114,"label":52,"slug":114,"count":5},"model-validation",{"key":116,"label":50,"slug":116,"count":5},"portfolio-analytics",{"key":118,"label":119,"slug":118,"count":5},"probability-of-default-pd-modelling","Probability Of Default (Pd) Modelling",{"key":121,"label":62,"slug":121,"count":5},"recovery-analysis",{"key":123,"label":54,"slug":123,"count":5},"regulatory-compliance",{"key":125,"label":55,"slug":125,"count":5},"statistical-modelling",{"key":127,"label":63,"slug":127,"count":5},"stress-testing",{"key":129,"label":58,"slug":129,"count":5},"survival-analysis",{"key":131,"label":60,"slug":131,"count":5},"time-series-analysis",[133,136,139,141,144,146,148,150,152],{"key":134,"label":135,"slug":134,"count":5},"aws","Aws",{"key":137,"label":138,"slug":137,"count":5},"cicd","Ci\u002FCd",{"key":140,"label":38,"slug":140,"count":5},"git",{"key":142,"label":143,"slug":142,"count":5},"numpy","Numpy",{"key":34,"label":145,"slug":34,"count":5},"Pandas",{"key":147,"label":32,"slug":147,"count":5},"python",{"key":36,"label":149,"slug":36,"count":5},"Scikit-Learn",{"key":151,"label":33,"slug":151,"count":5},"sql",{"key":37,"label":153,"slug":37,"count":5},"Statsmodels",[155],{"key":23,"label":23,"count":5},[157],{"key":26,"label":26,"count":5},{"total":159,"jobs":160},0,[]]