[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"seo-content-expertise-machine-learning-us":3,"job-search-\u002Fen-us\u002Fjobs-by-expertise\u002Fmachine-learning?page=1":33,"expertise-filters-\u002Fen-us\u002Fjobs-by-expertise\u002Fmachine-learning?page=1":677,"featured-jobs-\u002Fen-us\u002Fjobs-by-expertise\u002Fmachine-learning?page=1":837},{"image_path":4,"content_de":5,"content_en":18,"composite_key":31,"last_updated":32},"\u002Fimages\u002Fexpertise\u002Fmachine-learning.webp",{"tab1":6,"tab2":9,"tab3":12,"tab4":15},{"title":7,"content":8},"Bedeutung & Relevanz","Maschinelles Lernen hat sich zu einer Schlüsseltechnologie in den **Vereinigten Staaten** entwickelt, die die Finanzindustrie grundlegend verändert. Im Bereich **[Asset Management](\u002Fjobs-by-industry\u002Fasset-management)** ermöglicht **[Machine Learning](\u002Fjobs-by-skill\u002Fmachine-learning)** die Analyse riesiger Datenmengen für bessere Anlageentscheidungen und Risikoprognosen. Führende Unternehmen wie **CM003 American Century Services, LLC** und **Pacific Investment Mgt Co. LLC** setzen bereits intensiv auf diese Technologie.\n\nDie Relevanz zeigt sich besonders in der Automatisierung komplexer Prozesse wie **[Portfolio Management](\u002Fjobs-by-expertise\u002Fportfolio-management)** und **[Risk Management](\u002Fjobs-by-expertise\u002Frisk-management)**. Durch den Einsatz von **[Generative AI](\u002Fjobs-by-skill\u002Fgenerative-ai)** und **[Artificial Intelligence Tools](\u002Fjobs-by-skill\u002Fartificial-intelligence-tools)** können Finanzinstitute Muster in Marktdaten erkennen, die dem menschlichen Auge verborgen bleiben. Dies führt zu präziseren Vorhersagen und effizienteren Handelsstrategien.\n\nIm Versicherungssektor revolutioniert **[Machine Learning](\u002Fjobs-by-skill\u002Fmachine-learning)** die Schadensanalyse und Prämienkalkulation. Unternehmen wie **Allianz Insurance** nutzen Algorithmen für **[Financial Analysis](\u002Fjobs-by-expertise\u002Ffinancial-analysis)** und Betrugserkennung. Die Technologie unterstützt zudem bei regulatorischen Anforderungen durch automatisierte **[Financial Reporting](\u002Fjobs-by-expertise\u002Ffinancial-reporting)** und Compliance-Überwachung.",{"title":10,"content":11},"Top-Branchen & Standorte","Die Nachfrage nach **[Machine Learning](\u002Fjobs-by-skill\u002Fmachine-learning)**-Expertise konzentriert sich in den **Vereinigten Staaten** auf mehrere Schlüsselstandorte und Branchen. In **Kansas City** haben sich insbesondere Unternehmen des **[Asset Management](\u002Fjobs-by-industry\u002Fasset-management)** etabliert, die fortschrittliche Algorithmen für **[Portfolio Management](\u002Fjobs-by-expertise\u002Fportfolio-management)** entwickeln.\n\n**Chicago** als traditionelles Finanzzentrum bietet zahlreiche Möglichkeiten in **[Banking](\u002Fjobs-by-industry\u002Fbanking)** und **[Investment Management](\u002Fjobs-by-industry\u002Fasset-management-investment-management)**, wo **[Machine Learning](\u002Fjobs-by-skill\u002Fmachine-learning)** für Handelsstrategien und Risikomanagement eingesetzt wird. Die Stadt ist ein Hotspot für Fintech-Innovationen.\n\nIn **Newport Beach** dominieren Unternehmen des **[Asset Management Financial Services](\u002Fjobs-by-industry\u002Fasset-management-financial-services)**, die komplexe quantitative Modelle entwickeln. Der Versicherungssektor zeigt ebenfalls starkes Interesse, besonders für **[Actuarial Analysis](\u002Fjobs-by-expertise\u002Factuarial-analysis)** und Schadensvorhersage.\n\nWeitere bedeutende Branchen umfassen **[Financial Services](\u002Fjobs-by-industry\u002Fasset-managementfinancial-services)** mit Fokus auf **[Systematic Trading](\u002Fjobs-by-expertise\u002Fsystematic-trading)** und **[Financial Engineering](\u002Fjobs-by-expertise\u002Ffinancial-engineering)**. Die Technologie findet zunehmend Anwendung in **[Property & Casualty Insurance](\u002Fjobs-by-expertise\u002Fproperty-casualty-insurance)** für präzisere Risikobewertungen.",{"title":13,"content":14},"Gefragte Skills & Arbeitgeber","Führende Arbeitgeber in den **Vereinigten Staaten** wie **CM003 American Century Services, LLC**, **Allianz Insurance** und **Pacific Investment Mgt Co. LLC** suchen nach Experten mit **[Machine Learning](\u002Fjobs-by-skill\u002Fmachine-learning)**-Kenntnissen kombiniert mit spezifischem Fachwissen.\n\nGefragte Expertise umfasst **[Financial Modeling](\u002Fjobs-by-expertise\u002Ffinancial-modeling)** für die Entwicklung prädiktiver Modelle, **[Quantitative Research](\u002Fjobs-by-expertise\u002Fquantitative-research)** für Handelsstrategien und **[Data Analysis](\u002Fjobs-by-expertise\u002Fdata-analysis)** für Marktprognosen. Kenntnisse in **[Risk Assessment](\u002Fjobs-by-expertise\u002Frisk-assessment)** sind essentiell für regulatorische Anforderungen.\n\nIm technischen Bereich werden **[Python](\u002Fjobs-by-skill\u002Fpython)** und **[SQL](\u002Fjobs-by-skill\u002Fsql)** für Datenverarbeitung, **[Java](\u002Fjobs-by-skill\u002Fjava)** für skalierbare Systeme und **[R](\u002Fjobs-by-skill\u002Fr)** für statistische Analysen benötigt. Cloud-Kenntnisse in **[AWS](\u002Fjobs-by-skill\u002Faws)** und Erfahrung mit **[Kubernetes](\u002Fjobs-by-skill\u002Fkubernetes)** sind zunehmend wichtig.\n\nWeitere relevante Fähigkeiten umfassen **[Time-Series Analysis](\u002Fjobs-by-expertise\u002Ftime-series-analysis)** für Finanzdaten, **[Natural Language Processing](\u002Fjobs-by-expertise\u002Fnatural-language-processing)** für Nachrichtenanalyse und **[Pattern Recognition](\u002Fjobs-by-expertise\u002Fpattern-recognition)** für Handelssignale. Tools wie **FactSet** und **Bloomberg** ergänzen die technische Ausstattung.",{"title":16,"content":17},"Karriere & Entwicklung","Karrierewege mit **[Machine Learning](\u002Fjobs-by-skill\u002Fmachine-learning)** in den **Vereinigten Staaten** bieten vielfältige Entwicklungsmöglichkeiten. Einstiegspositionen wie **Data Scientist** oder **Quantitative Analyst** führen zu Rollen wie **Machine Learning Engineer** oder **AI Research Lead** in führenden Finanzinstituten.\n\nFür den Erfolg sind fundierte Kenntnisse in **[Python](\u002Fjobs-by-skill\u002Fpython)** und **[SQL](\u002Fjobs-by-skill\u002Fsql)** essentiell, ergänzt durch **[Relational Databases](\u002Fjobs-by-skill\u002Frelational-databases)** und **[Object-Oriented Programming](\u002Fjobs-by-skill\u002Fobject-oriented-programming)**. Fortgeschrittene Themen wie **[Generative AI](\u002Fjobs-by-skill\u002Fgenerative-ai)** und **[Artificial Intelligence Tools](\u002Fjobs-by-skill\u002Fartificial-intelligence-tools)** öffnen Türen zu innovativen Projekten.\n\nPraktische Erfahrung mit **[Financial Markets](\u002Fjobs-by-expertise\u002Ffinancial-markets)** und **[Investment Strategy](\u002Fjobs-by-expertise\u002Finvestment-strategy)** ist ebenso wichtig wie technisches Verständnis von **[Cloud\u002FAWS](\u002Fjobs-by-skill\u002Fcloud-aws)** und **[CI\u002FCD](\u002Fjobs-by-skill\u002Fci-cd)**-Prozessen. Spezialisierungen in **[Portfolio Construction](\u002Fjobs-by-expertise\u002Fportfolio-construction)** oder **[Alpha Signal Development](\u002Fjobs-by-expertise\u002Falpha-signal-development)** bieten Wettbewerbsvorteile.\n\nWeiterbildungen sollten **[Statistics](\u002Fjobs-by-skill\u002Fstatistics)** und **[Probability](\u002Fjobs-by-skill\u002Fprobability)** vertiefen, kombiniert mit domänenspezifischem Wissen in **[IFRS 4](\u002Fjobs-by-expertise\u002Fifrs-4)** und **[IFRS 17](\u002Fjobs-by-expertise\u002Fifrs-17)** für Versicherungsanwendungen. Agile Methoden wie **[Agile\u002FSAFe](\u002Fjobs-by-skill\u002Fagile-safe)** runden das Profil ab.",{"tab1":19,"tab2":22,"tab3":25,"tab4":28},{"title":20,"content":21},"Strategic Importance","Machine Learning has become a transformative force in the United States financial sector, driving innovation across asset management, insurance, and investment management. In the competitive landscape of American financial services, ML algorithms enable firms to analyze vast datasets for predictive insights, automate complex processes, and enhance decision-making capabilities. Leading companies like [Allianz Insurance](\u002Fcompany\u002Fallianz-insurance) and [Pacific Investment Mgt Co. LLC](\u002Fcompany\u002Fpacific-investment-mgt-co-llc) leverage ML for everything from risk assessment to portfolio optimization, giving them significant competitive advantages. The strategic importance extends beyond operational efficiency to revenue generation through algorithmic trading strategies and personalized financial product recommendations. As regulatory requirements like IFRS 17 and quarterly closing demand more sophisticated analysis, ML provides the computational power to meet these challenges while maintaining compliance. Financial institutions across the United States are investing heavily in ML capabilities, recognizing that mastery of this technology is no longer optional but essential for market leadership and sustainable growth in today's data-driven economy.",{"title":23,"content":24},"Top Industries & Locations","Machine Learning expertise is particularly sought after in key American financial hubs and specialized sectors. In the [Asset Management](\u002Fjobs-by-industry\u002Fasset-management) and [Banking](\u002Fjobs-by-industry\u002Fbanking) industries, ML drives alpha generation and risk management strategies, while the insurance sector uses it for predictive modeling and claims analysis. Major demand centers include [Kansas City](\u002Fjobs-in-kansas-city), home to [CM003 American Century Services, LLC](\u002Fcompany\u002Fcm003-american-century-services-llc), where ML powers investment research and portfolio management. [Chicago](\u002Fjobs-in-chicago) serves as a hub for quantitative finance firms applying ML to market analysis and systematic trading. [Newport Beach](\u002Fjobs-in-newport-beach) hosts sophisticated investment managers like [Pacific Investment Mgt Co. LLC](\u002Fcompany\u002Fpacific-investment-mgt-co-llc) that employ ML for fixed income markets and portfolio construction. The concentration of ML talent in these locations reflects the specialized nature of financial applications, where understanding market dynamics and financial products combines with technical expertise to create competitive advantages in the United States financial landscape.",{"title":26,"content":27},"In-Demand Skills & Employers","Leading American financial institutions seek professionals who combine Machine Learning with domain-specific expertise. Top employers like [CM003 American Century Services, LLC](\u002Fcompany\u002Fcm003-american-century-services-llc), [Allianz Insurance](\u002Fcompany\u002Fallianz-insurance), and [Pacific Investment Mgt Co. LLC](\u002Fcompany\u002Fpacific-investment-mgt-co-llc) require ML specialists with complementary skills in [Risk Management](\u002Fjobs-by-expertise\u002Frisk-management), [Portfolio Management](\u002Fjobs-by-expertise\u002Fportfolio-management), and [Financial Analysis](\u002Fjobs-by-expertise\u002Ffinancial-analysis). These companies value expertise in tools like FactSet and Bloomberg for market data integration, combined with ML applications for pattern recognition and alpha signal development. The most sought-after professionals understand both the technical aspects of ML and the financial context, including knowledge of IFRS standards, regulatory compliance, and financial markets. Experience with property & casualty insurance analysis, loss ratio modeling, and reinsurance knowledge is particularly valuable in the insurance sector, while investment firms prioritize skills in quantitative research, time-series analysis, and financial engineering. The integration of [Artificial Intelligence Tools](\u002Fjobs-by-skill\u002Fartificial-intelligence-tools) and natural language processing with traditional financial expertise creates the most compelling value proposition for employers across the United States.",{"title":29,"content":30},"Career & Development","Building a Machine Learning career in the United States financial sector requires a strategic combination of technical mastery and financial domain knowledge. Entry paths often begin with strong foundations in [Python](\u002Fjobs-by-skill\u002Fpython) programming, [SQL](\u002Fjobs-by-skill\u002Fsql) for data manipulation, and statistical analysis using R, progressing to advanced ML techniques and cloud platforms like AWS. Professionals should develop expertise in relevant financial domains such as [Financial Modeling](\u002Fjobs-by-expertise\u002Ffinancial-modeling), actuarial analysis, and investment strategy while mastering ML applications for specific use cases. Career advancement typically involves specialization in areas like algorithmic trading, risk modeling, or insurance analytics, with senior roles requiring leadership in ML implementation and strategy. Continuous learning through certifications in [Generative AI](\u002Fjobs-by-skill\u002Fgenerative-ai), Kubernetes for deployment, and financial regulatory frameworks ensures ongoing relevance. The most successful ML professionals in finance combine technical skills with deep understanding of market dynamics, financial products, and business objectives, creating solutions that drive tangible value for American financial institutions navigating increasingly complex and data-intensive environments.","expertise:machine-learning:us","2026-08-17T09:09:34.265401+02:00",{"total":34,"jobs":35},48,[36,98,131,174,202,239,264,307,341,355,372,423,467,507,518,545,567,586,632,658],{"id":37,"slug":38,"title":39,"raw_title":40,"is_featured":41,"company":42,"raw_hiringOrganization_logo_url":40,"processed_city_gmaps":45,"processed_country_iso_code_gmaps":47,"processed_home_office":49,"processed_salary_min":50,"processed_salary_max":51,"processed_salary_currency":52,"processed_salary_source":53,"processed_working_hours":54,"processed_employment_types":56,"processed_it_skills":57,"processed_it_skills_labels":61,"processed_job_expertise_skills":76,"processed_job_expertise_skills_labels":77,"max_cpc":96,"actual_cpc":97},92767,"senior-data-scientist-great-american-insurance-company","Senior Data Scientist",null,false,{"name":43,"slug":44,"logo_url":40},"Great American Insurance Company","great-american-insurance-company",[46],"Cincinnati",[48],"US","hybrid",120000,160000,"USD","estimated",[55],"FULL_TIME","permanent",{"r":58,"aws":59,"dbt":59,"gcp":59,"sql":60,"azure":59,"python":60,"airflow":59,"tableau":59,"xgboost":60,"lightgbm":60,"power bi":59,"snowflake":60,"scikit-learn":60},2,3,4,[62,63,64,65,66,67,68,69,70,71,72,73,74,75],"Python","scikit-learn","XGBoost","LightGBM","R","SQL","Snowflake","AWS","Azure","GCP","Airflow","dbt","Tableau","Power BI",{"claims":59,"pricing":59,"telematics":58,"data science":60,"underwriting":59,"agile planning":59,"geospatial data":58,"machine learning":60,"model deployment":60,"model governance":60,"model monitoring":60,"actuarial science":59,"data visualization":60,"predictive modeling":60,"catastrophe modeling":58,"dashboard development":60,"regulatory compliance":60,"data pipeline development":60},[78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95],"Predictive Modeling","Machine Learning","Data Science","Model Deployment","Model Monitoring","Data Pipeline Development","Dashboard Development","Data Visualization","Agile Planning","Model Governance","Regulatory Compliance","Actuarial Science","Underwriting","Claims","Pricing","Geospatial Data","Telematics","Catastrophe Modeling",1,1.5,{"id":99,"slug":100,"title":101,"raw_title":40,"is_featured":41,"company":102,"raw_hiringOrganization_logo_url":40,"processed_city_gmaps":105,"processed_country_iso_code_gmaps":107,"processed_home_office":49,"processed_salary_min":108,"processed_salary_max":109,"processed_salary_currency":52,"processed_salary_source":53,"processed_working_hours":110,"processed_employment_types":56,"processed_it_skills":111,"processed_it_skills_labels":112,"processed_job_expertise_skills":114,"processed_job_expertise_skills_labels":115,"max_cpc":96,"actual_cpc":97},99611,"fraud-data-scientist-umb-bank-na","Fraud Data Scientist",{"name":103,"slug":104,"logo_url":40},"UMB BANK NA","umb-bank-na",[106],"Kansas City",[48],98208,144705,[55],{"r":60,"python":60,"tableau":59,"power bi":59,"snowflake":59,"sql server":59},[66,62,68,113,74,75],"SQL Server",{"clustering":60,"a\u002Fb testing":60,"data mining":60,"deep learning":59,"fraud modeling":59,"data frameworks":59,"fraud reporting":59,"machine learning":60,"automation systems":59,"propensity scoring":59,"predictive modeling":60,"statistical analysis":60,"customer segmentation":60,"hyperparameter tuning":60,"recommendation systems":59,"natural language processing":59,"banking or financial services":58},[116,79,117,78,118,119,120,121,122,123,124,125,126,127,128,129,130],"Statistical Analysis","Deep Learning","Hyperparameter Tuning","A\u002FB Testing","Recommendation Systems","Propensity Scoring","Natural Language Processing","Clustering","Customer Segmentation","Data Mining","Automation Systems","Data Frameworks","Fraud Modeling","Fraud Reporting","Banking or Financial Services",{"id":132,"slug":133,"title":134,"raw_title":40,"is_featured":41,"company":135,"raw_hiringOrganization_logo_url":40,"processed_city_gmaps":138,"processed_country_iso_code_gmaps":140,"processed_home_office":141,"processed_salary_min":142,"processed_salary_max":143,"processed_salary_currency":52,"processed_salary_source":53,"processed_working_hours":144,"processed_employment_types":56,"processed_it_skills":145,"processed_it_skills_labels":146,"processed_job_expertise_skills":159,"processed_job_expertise_skills_labels":160,"max_cpc":96,"actual_cpc":97},93821,"quantitative-researcher-invesco-advisers-inc","Quantitative Researcher",{"name":136,"slug":137,"logo_url":40},"Invesco Advisers, Inc.","invesco-advisers-inc",[139],"Atlanta",[48],"onsite",150000,250000,[55],{"git":59,"ice":58,"sql":60,"numpy":60,"scipy":60,"axioma":58,"pandas":60,"polars":59,"python":60,"aladdin":58,"barraone":58,"bloomberg":58,"statsmodels":60,"bloomberg port":58},[62,67,147,148,149,150,151,152,153,154,155,156,157,158],"Git","pandas","Polars","NumPy","SciPy","statsmodels","Aladdin","Bloomberg PORT","BarraOne","Axioma","Bloomberg","ICE",{"cfa":58,"factor models":60,"risk management":60,"asset allocation":60,"machine learning":59,"scenario analysis":60,"fixed income markets":60,"quantitative research":60,"macroeconomic analysis":60,"portfolio construction":60,"portfolio optimization":60,"performance attribution":60,"relative value analysis":60,"time-series econometrics":60},[161,162,163,164,165,166,167,168,169,170,171,172,79,173],"Quantitative Research","Fixed Income Markets","Macroeconomic Analysis","Relative Value Analysis","Portfolio Construction","Portfolio Optimization","Risk Management","Scenario Analysis","Performance Attribution","Factor Models","Time-Series Econometrics","Asset Allocation","CFA",{"id":175,"slug":176,"title":177,"raw_title":40,"is_featured":41,"company":178,"raw_hiringOrganization_logo_url":40,"processed_city_gmaps":181,"processed_country_iso_code_gmaps":183,"processed_home_office":49,"processed_salary_min":184,"processed_salary_max":185,"processed_salary_currency":52,"processed_salary_source":53,"processed_working_hours":186,"processed_employment_types":56,"processed_it_skills":187,"processed_it_skills_labels":188,"processed_job_expertise_skills":191,"processed_job_expertise_skills_labels":192,"max_cpc":96,"actual_cpc":97},94458,"quantitative-analytics-associate-keybank-national-association-2","Quantitative Analytics Associate",{"name":179,"slug":180,"logo_url":40},"KeyBank National Association","keybank-national-association",[182],"Brooklyn",[48],69000,105000,[55],{"r":59,"sas":59,"python":59,"microsoft office suite":59},[62,66,189,190],"SAS","Microsoft Office Suite",{"data literacy":59,"data wrangling":59,"machine learning":58,"model validation":58,"big data analysis":58,"model development":58,"data visualization":58,"diagnostic testing":58,"predictive modeling":58,"quantitative analysis":59,"descriptive statistics":59,"probability distributions":59},[193,194,195,196,85,197,198,79,78,199,200,201],"Quantitative Analysis","Data Wrangling","Descriptive Statistics","Probability Distributions","Model Development","Model Validation","Data Literacy","Diagnostic Testing","Big Data Analysis",{"id":203,"slug":204,"title":205,"raw_title":40,"is_featured":41,"company":206,"raw_hiringOrganization_logo_url":40,"processed_city_gmaps":209,"processed_country_iso_code_gmaps":211,"processed_home_office":49,"processed_salary_min":212,"processed_salary_max":213,"processed_salary_currency":52,"processed_salary_source":53,"processed_working_hours":214,"processed_employment_types":56,"processed_it_skills":215,"processed_it_skills_labels":216,"processed_job_expertise_skills":229,"processed_job_expertise_skills_labels":230,"max_cpc":96,"actual_cpc":97},92310,"senior-data-scientist-assistant-vice-president-6301-db-usa-core-corporation","Senior Data Scientist - Assistant Vice President",{"name":207,"slug":208,"logo_url":40},"6301 DB USA Core Corporation","6301-db-usa-core-corporation",[210],"New York",[48],100000,153000,[55],{"sql":60,"apis":59,"mlops":59,"python":60,"testing":59,"deployment":59,"generative ai":59,"data pipelines":59,"microsoft azure":59,"version control":59,"amazon web services (aws)":59,"machine learning libraries":60,"google cloud platform (gcp)":59,"large language models (llm)":59},[62,67,217,218,219,220,221,222,223,224,225,226,227,228],"Google Cloud Platform (GCP)","Microsoft Azure","Amazon Web Services (AWS)","Machine Learning Libraries","Version Control","Testing","Deployment","Generative AI","Large Language Models (LLM)","MLOps","APIs","Data Pipelines",{"data science":60,"model testing":60,"financial crime":59,"machine learning":60,"model deployment":60,"model monitoring":59,"model validation":60,"model development":60,"advanced analytics":60,"data identification":59,"requirements gathering":60,"statistical techniques":60,"transaction monitoring":59,"wealth management analytics":59},[80,79,231,232,233,197,234,198,81,235,236,237,82,238],"Advanced Analytics","Statistical Techniques","Requirements Gathering","Model Testing","Transaction Monitoring","Wealth Management Analytics","Financial Crime","Data Identification",{"id":240,"slug":241,"title":242,"raw_title":40,"is_featured":41,"company":243,"raw_hiringOrganization_logo_url":40,"processed_city_gmaps":246,"processed_country_iso_code_gmaps":248,"processed_home_office":49,"processed_salary_min":213,"processed_salary_max":249,"processed_salary_currency":52,"processed_salary_source":53,"processed_working_hours":250,"processed_employment_types":56,"processed_it_skills":251,"processed_it_skills_labels":252,"processed_job_expertise_skills":254,"processed_job_expertise_skills_labels":255,"max_cpc":96,"actual_cpc":97},97878,"director-data-science-sun-life-financial-us-services-company-inc-2","Director, Data Science",{"name":244,"slug":245,"logo_url":40},"Sun Life Financial (U.S.) Services Company, Inc.","sun-life-financial-us-services-company-inc",[247],"Wellesley",[48],229500,[55],{"sql":60,"python":60,"dataiku":60,"snowflake":60},[62,67,68,253],"Dataiku",{"pricing":60,"data science":60,"underwriting":60,"responsible ai":60,"risk selection":60,"data compliance":60,"machine learning":60,"model deployment":60,"model governance":60,"model monitoring":60,"agile methodology":59,"data visualization":60,"information security":60,"portfolio management":60,"statistical modeling":60,"artificial intelligence":60},[80,79,256,90,92,257,258,87,259,260,261,262,85,81,82,263],"Artificial Intelligence","Risk Selection","Portfolio Management","Responsible AI","Data Compliance","Information Security","Statistical Modeling","Agile Methodology",{"id":265,"slug":266,"title":267,"raw_title":40,"is_featured":41,"company":268,"raw_hiringOrganization_logo_url":40,"processed_city_gmaps":271,"processed_country_iso_code_gmaps":273,"processed_home_office":141,"processed_salary_min":274,"processed_salary_max":275,"processed_salary_currency":52,"processed_salary_source":53,"processed_working_hours":276,"processed_employment_types":56,"processed_it_skills":277,"processed_it_skills_labels":278,"processed_job_expertise_skills":282,"processed_job_expertise_skills_labels":283,"max_cpc":96,"actual_cpc":97},90506,"intraday-liquidity-analyst-bank-of-montreal-2","Intraday Liquidity Analyst",{"name":269,"slug":270,"logo_url":40},"Bank of Montreal","bank-of-montreal",[272],"Chicago",[48],65000,140000,[55],{"r":58,"sql":60,"vba":59,"excel":60,"access":59,"python":58},[279,280,67,281,62,66],"Excel","Access","VBA",{"pricing":60,"reporting":60,"algorithms":59,"automation":60,"valuations":60,"a\u002Fb testing":59,"policy input":60,"data 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