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sigma":30,"kpi development":30,"financial acumen":29,"dashboard creation":29,"project management":29,"strategic planning":30,"process improvement":30,"operational excellence":30},[511,512,134,95,513,514,515,516,133,517],"Lean Six Sigma","Operational Excellence","KPI Development","Dashboard Creation","Strategic Planning","Financial Acumen","Consulting",{"id":519,"slug":520,"title":521,"raw_title":10,"is_featured":11,"company":522,"raw_hiringOrganization_logo_url":10,"processed_city_gmaps":525,"processed_country_iso_code_gmaps":526,"processed_home_office":19,"processed_salary_min":527,"processed_salary_max":528,"processed_salary_currency":22,"processed_salary_source":23,"processed_working_hours":529,"processed_employment_types":26,"processed_it_skills":530,"processed_it_skills_labels":531,"processed_job_expertise_skills":536,"processed_job_expertise_skills_labels":537,"max_cpc":70,"actual_cpc":71},60136,"solution-consultant-usa016-refinitiv-us-llc","Solution Consultant",{"name":523,"slug":524,"logo_url":10},"Refinitiv US LLC","refinitiv-us-llc",[],[18],86300,143900,[25],{"java":29,"python":30,"ai tools":29,"data science":30,"api technologies":30,"machine learning":29,"cloud platforms (aws, azure)":29},[153,191,532,533,534,39,535],"API Technologies","Cloud Platforms (AWS, Azure)","Data Science","AI Tools",{"risk scoring":29,"data modeling":30,"data wrangling":30,"data enrichment":30,"model deployment":30,"anomaly detection":29,"data transformation":30,"feature engineering":30,"screening platforms":29,"solution consulting":30,"sanctions compliance":30,"watchlist management":29,"adverse media analytics":29,"sales process management":30,"anti-money laundering (aml)":30,"third-party risk management":30,"regulatory knowledge (ofac, fincen, fatf)":30},[538,539,540,541,542,543,544,545,546,547,548,549,550,551,552,553,554],"Anti-Money Laundering (AML)","Sanctions Compliance","Third-Party Risk Management","Data Wrangling","Feature Engineering","Model Deployment","Data Modeling","Data Transformation","Data Enrichment","Solution Consulting","Sales Process Management","Regulatory Knowledge (OFAC, FinCEN, FATF)","Screening Platforms","Watchlist Management","Adverse Media Analytics","Risk Scoring","Anomaly Detection",{"id":556,"slug":557,"title":558,"raw_title":10,"is_featured":11,"company":559,"raw_hiringOrganization_logo_url":10,"processed_city_gmaps":562,"processed_country_iso_code_gmaps":564,"processed_home_office":110,"processed_salary_min":20,"processed_salary_max":565,"processed_salary_currency":22,"processed_salary_source":23,"processed_working_hours":566,"processed_employment_types":26,"processed_it_skills":567,"processed_it_skills_labels":568,"processed_job_expertise_skills":574,"processed_job_expertise_skills_labels":575,"max_cpc":70,"actual_cpc":71},49172,"senior-security-engineer-ai-vice-president-mufg-bank-ltd","Senior Security Engineer - AI, Vice President",{"name":560,"slug":561,"logo_url":10},"MUFG Bank, Ltd.","mufg-bank-ltd",[563],"Jersey City",[18],203000,[25],{"aws":29,"git":29,"azure":29,"crewai":30,"docker":29,"python":30,"autogpt":30,"graphql":30,"langchain":30,"kubernetes":29,"restful apis":30,"machine learning":30,"vector databases":30},[153,39,569,570,571,266,572,339,338,573,34,216,342],"LangChain","CrewAI","AutoGPT","Kubernetes","GraphQL",{"mlops":30,"compliance":30,"cybersecurity":30,"ai agent design":30,"ml model tuning":30,"process mapping":30,"prompt engineering":30,"data pipeline orchestration":30},[576,577,379,578,579,580,581,242],"Cybersecurity","AI Agent Design","ML Model Tuning","Data Pipeline Orchestration","Process Mapping","MLOps",{"id":583,"slug":584,"title":585,"raw_title":10,"is_featured":11,"company":586,"raw_hiringOrganization_logo_url":10,"processed_city_gmaps":589,"processed_country_iso_code_gmaps":590,"processed_home_office":110,"processed_salary_min":20,"processed_salary_max":591,"processed_salary_currency":22,"processed_salary_source":23,"processed_working_hours":592,"processed_employment_types":26,"processed_it_skills":593,"processed_it_skills_labels":594,"processed_job_expertise_skills":598,"processed_job_expertise_skills_labels":599,"max_cpc":70,"actual_cpc":71},49432,"associate-systematic-credit-research-quant-mufg-securities-americas-inc","Associate, Systematic Credit Research Quant",{"name":587,"slug":588,"logo_url":10},"MUFG Securities Americas Inc.","mufg-securities-americas-inc",[290],[18],171000,[25],{"python":30,"machine learning":30,"numerical methods":29,"statistical modeling":30,"optimization algorithms":29},[153,39,595,596,597],"Statistical Modeling","Numerical Methods","Optimization Algorithms",{"bonds":29,"trading algorithms":30,"stochastic calculus":30,"structured products":29,"credit default swaps":29,"credit risk modeling":30,"optimization methods":30,"time series analysis":30,"relative value strategies":30},[600,601,602,603,604,605,606,607,608],"Credit Risk Modeling","Trading Algorithms","Relative Value Strategies","Credit Default Swaps","Bonds","Structured Products","Stochastic Calculus","Time Series Analysis","Optimization Methods",{"id":610,"slug":611,"title":612,"raw_title":10,"is_featured":11,"company":613,"raw_hiringOrganization_logo_url":10,"processed_city_gmaps":614,"processed_country_iso_code_gmaps":616,"processed_home_office":110,"processed_salary_min":617,"processed_salary_max":618,"processed_salary_currency":22,"processed_salary_source":23,"processed_working_hours":619,"processed_employment_types":26,"processed_it_skills":620,"processed_it_skills_labels":621,"processed_job_expertise_skills":624,"processed_job_expertise_skills_labels":625,"max_cpc":70,"actual_cpc":71},80117,"pc-actuary-director-reserving-allstate-insurance-co","P&C Actuary Director - Reserving",{"name":143,"slug":144,"logo_url":10},[615],"Cook County",[18],190000,250000,[25],{"r":29,"ai":29,"python":29,"copilot":29,"generative ai":29,"machine learning":29},[153,622,40,39,442,623],"R","Copilot",{"erm":30,"cape cod":30,"chain-ladder":30,"compensation":30,"ai governance":29,"talent management":30,"capital management":30,"program management":30,"succession planning":30,"bornhuetter-ferguson":30,"stochastic reserving":30,"model risk management":29,"organizational design":30,"loss reserving methodologies":30,"individual claim-level models":30,"insurance reserving frameworks":30,"actuarial\u002Fstatistical techniques":30,"regulatory requirements (gaap, statutory)":30},[626,627,628,629,630,631,632,633,634,635,636,637,638,639,640,641,642,643],"Loss Reserving Methodologies","Chain-Ladder","Bornhuetter-Ferguson","Cape Cod","Stochastic Reserving","Individual Claim-Level Models","Actuarial\u002FStatistical Techniques","Insurance Reserving Frameworks","ERM","Capital Management","Regulatory Requirements (GAAP, Statutory)","AI Governance","Model Risk Management","Talent Management","Succession Planning","Compensation","Organizational Design","Program Management",{"id":645,"slug":646,"title":647,"raw_title":10,"is_featured":11,"company":648,"raw_hiringOrganization_logo_url":10,"processed_city_gmaps":649,"processed_country_iso_code_gmaps":650,"processed_home_office":110,"processed_salary_min":258,"processed_salary_max":112,"processed_salary_currency":22,"processed_salary_source":23,"processed_working_hours":651,"processed_employment_types":26,"processed_it_skills":652,"processed_it_skills_labels":653,"processed_job_expertise_skills":655,"processed_job_expertise_skills_labels":656,"max_cpc":70,"actual_cpc":71},52633,"intelligent-automation-developer-auto-owners-insurance-company","Intelligent Automation Developer",{"name":178,"slug":179,"logo_url":10},[181],[18],[25],{"nlp":29,"rpa":29,"java":29,"ocr\u002Ficr":29,"data analysis":29,"machine learning":29,"agile software development":29},[188,39,189,190,95,191,654],"Agile software development",{"system analysis":29,"software testing":29,"knowledge extraction":29,"software development":30,"document understanding":29},[195,196,199,657,658],"Document Understanding","Knowledge Extraction",{"image_path":660,"content_de":661,"content_en":674,"composite_key":687,"last_updated":688},"\u002Fimages\u002Fskill\u002Fmachine-learning.webp",{"tab1":662,"tab2":665,"tab3":668,"tab4":671},{"title":663,"content":664},"Bedeutung & Relevanz","**Maschinelles Lernen** (Machine Learning) hat in den **Vereinigten Staaten** eine enorme strategische Bedeutung erlangt, insbesondere in der Finanz- und Versicherungsbranche. Diese Technologie revolutioniert traditionelle Prozesse durch datengesteuerte Entscheidungsfindung und automatisierte Analysen. In der **Versicherungsbranche** ([Insurance](\u002Fjobs-by-industry\u002Finsurance)) ermöglicht maschinelles Lernen präzisere Risikobewertungen, Betrugserkennung in Echtzeit und personalisierte Policen. Bei **Investment Management** ([Investment Management](\u002Fjobs-by-industry\u002Finvestment-management)) optimiert es Portfoliostrategien, identifiziert Markttrends und verbessert die Anlageperformance durch prädiktive Analysen.\n\nFührende Unternehmen wie **Allianz Insurance** und **Pacific Investment Mgt Co. LLC** setzen maschinelles Lernen ein, um Wettbewerbsvorteile zu sichern und operative Effizienz zu steigern. Die Technologie unterstützt komplexe Aufgaben wie **Risikomodellierung** ([Risk Modeling](\u002Fjobs-by-expertise\u002Frisk-modeling)), **Schadenmodellierung** ([Loss Modeling](\u002Fjobs-by-expertise\u002Floss-modeling)) und **Katastrophenanalyse** ([Catastrophe Analysis](\u002Fjobs-by-expertise\u002Fcatastrophe-analysis)), was in volatilen Märkten entscheidend ist. Mit der zunehmenden Datenmenge und Rechenleistung wird maschinelles Lernen weiterhin Wachstum und Innovation in den **Vereinigten Staaten** antreiben.",{"title":666,"content":667},"Top-Branchen & Standorte","In den **Vereinigten Staaten** ist die Nachfrage nach **Maschinelles Lernen** in Schlüsselbranchen und strategischen Standorten besonders hoch. Zu den Top-Branchen gehören **Versicherung** ([Insurance](\u002Fjobs-by-industry\u002Finsurance)) und **Investment Management** ([Investment Management](\u002Fjobs-by-industry\u002Finvestment-management)), wo maschinelles Lernen für Risikomanagement, Kundenanalysen und Prozessoptimierung eingesetzt wird.\n\nBei den Standorten ragen **Alpharetta** ([Alpharetta](\u002Fjobs-in-alpharetta)), **Chicago** ([Chicago](\u002Fjobs-in-chicago)) und **Newport Beach** ([Newport Beach](\u002Fjobs-in-newport-beach)) hervor. **Alpharetta** ist ein aufstrebendes Technologiezentrum mit vielen FinTech-Unternehmen, die maschinelles Lernen für innovative Lösungen nutzen. **Chicago**, als globaler Finanzhub, bietet zahlreiche Möglichkeiten in Banken und Versicherungen, die diese Technologie für Handelsalgorithmen und Compliance einsetzen. **Newport Beach** hingegen ist bekannt für seine Investmentfirmen, die maschinelles Lernen zur Portfoliosteuerung und Marktprognose verwenden. Diese Standorte kombinieren Infrastruktur, Talentpool und Branchenvielfalt, um maschinelles Lernen voranzutreiben.",{"title":669,"content":670},"Gefragte Skills & Arbeitgeber","In den **Vereinigten Staaten** verlangen Top-Arbeitgeber wie **Allianz Insurance** und **Pacific Investment Mgt Co. LLC** nach **Maschinelles Lernen** in Verbindung mit spezifischen Expertisen. Dazu gehören **Risikomanagement** ([Risk Management](\u002Fjobs-by-expertise\u002Frisk-management)), **Schadenbearbeitung** ([Claims Handling](\u002Fjobs-by-expertise\u002Fclaims-handling)), **Kunden-Onboarding** ([Client Onboarding](\u002Fjobs-by-expertise\u002Fclient-onboarding)) und **Portfolioanalyse** ([Portfolio Analysis](\u002Fjobs-by-expertise\u002Fportfolio-analysis)). Diese Fähigkeiten ermöglichen es, maschinelles Lernen für praktische Anwendungen wie Betrugserkennung, Kundenbindung und Anlagestrategien zu nutzen.\n\nWeitere gefragte Expertisen sind **Aktuariatswissenschaft** ([Actuarial Science](\u002Fjobs-by-expertise\u002Factuarial-science)), **technische Preisgestaltung** ([Technical Pricing](\u002Fjobs-by-expertise\u002Ftechnical-pricing)) und **quantitative Forschung** ([Quantitative Research](\u002Fjobs-by-expertise\u002Fquantitative-research)), die in Versicherungs- und Investmentkontexten unverzichtbar sind. Arbeitgeber integrieren maschinelles Lernen in **Geschäftsprozessverbesserung** ([Business Process Improvement](\u002Fjobs-by-expertise\u002Fbusiness-process-improvement)) und **Kundenerlebnismanagement** ([Customer Experience Management](\u002Fjobs-by-expertise\u002Fcustomer-experience-management)), um Effizienz und Servicequalität zu steigern. Diese Kombination aus technischem Know-how und domänenspezifischem Wissen macht Fachkräfte in den **Vereinigten Staaten** besonders wertvoll.",{"title":672,"content":673},"Karriere & Entwicklung","Karrierewege mit **Maschinelles Lernen** in den **Vereinigten Staaten** sind vielfältig und erfordern eine Mischung aus IT-Fähigkeiten und branchenspezifischem Wissen. Typische Rollen umfassen Data Scientist, ML Engineer oder Quantitative Analyst in Branchen wie **Versicherung** ([Insurance](\u002Fjobs-by-industry\u002Finsurance)) und **Investment Management** ([Investment Management](\u002Fjobs-by-industry\u002Finvestment-management)). Um einzusteigen, sollten Interessenten **Künstliche Intelligenz** ([Artificial Intelligence](\u002Fjobs-by-skill\u002Fartificial-intelligence)), **Python** ([Python](\u002Fjobs-by-skill\u002Fpython)) und **Datenanalyse** ([Data Analysis](\u002Fjobs-by-skill\u002Fdata-analysis)) beherrschen, da diese Grundlagen für Modellentwicklung und -implementierung entscheidend sind.\n\nFortgeschrittene Fähigkeiten wie **Risikomodellierung** ([Risk Modeling](\u002Fjobs-by-expertise\u002Frisk-modeling)), **Finanzmodellierung** ([Financial Modeling](\u002Fjobs-by-expertise\u002Ffinancial-modeling)) und **Zeitreihenanalyse** ([Time Series Analysis](\u002Fjobs-by-expertise\u002Ftime-series-analysis)) sind in Verbindung mit Tools wie **Matlab** ([Matlab](\u002Fjobs-by-skill\u002Fmatlab)) und **Preisgestaltungstools** ([Pricing Tools](\u002Fjobs-by-skill\u002Fpricing-tools)) gefragt. Praktische Erfahrung durch Projekte oder Zertifizierungen in **Datenwissenschaft** ([Data Science](\u002Fjobs-by-skill\u002Fdata-science)) und **Programmierung** ([Programming](\u002Fjobs-by-skill\u002Fprogramming)) kann den Einstieg erleichtern. Kontinuierliches Lernen und Anpassung an neue Technologien sind essenziell, um in diesem dynamischen Feld in den **Vereinigten Staaten** erfolgreich zu sein.",{"tab1":675,"tab2":678,"tab3":681,"tab4":684},{"title":676,"content":677},"Strategic Importance","Machine learning has become strategically vital across the financial sector in the [United States](\u002Fsearch), transforming how institutions analyze data, manage risk, and optimize operations. In [investment management](\u002Fjobs-by-industry\u002Finvestment-management), ML algorithms enhance [portfolio analysis](\u002Fjobs-by-expertise\u002Fportfolio-analysis) and [asset allocation](\u002Fjobs-by-expertise\u002Fasset-allocation), enabling more precise predictions of market movements and better investment decisions. The [insurance](\u002Fjobs-by-industry\u002Finsurance) industry leverages ML for advanced [risk modeling](\u002Fjobs-by-expertise\u002Frisk-modeling), [claims handling](\u002Fjobs-by-expertise\u002Fclaims-handling), and [technical pricing](\u002Fjobs-by-expertise\u002Ftechnical-pricing), improving accuracy in underwriting and fraud detection. Companies like [Allianz Insurance](\u002Fcompany\u002Fallianz-insurance) and [Pacific Investment Mgt Co. LLC](\u002Fcompany\u002Fpacific-investment-mgt-co-llc) are at the forefront, using ML to drive innovation in [financial markets](\u002Fjobs-by-expertise\u002Ffinancial-markets) and [customer experience management](\u002Fjobs-by-expertise\u002Fcustomer-experience-management). As data volumes grow, ML's role in [financial analysis](\u002Fjobs-by-expertise\u002Ffinancial-analysis) and [business process improvement](\u002Fjobs-by-expertise\u002Fbusiness-process-improvement) ensures competitive advantage, making it indispensable for firms navigating complex [multinational business](\u002Fjobs-by-expertise\u002Fmultinational-business) environments in the U.S.",{"title":679,"content":680},"Top Industries & Locations","Machine learning expertise is highly sought after in key U.S. financial hubs and industries. Top locations include [Alpharetta](\u002Fjobs-in-alpharetta), a growing tech and finance corridor; [Chicago](\u002Fsearch), a major center for trading and [risk management](\u002Fjobs-by-expertise\u002Frisk-management); and [Newport Beach](\u002Fsearch), known for investment firms and [asset management](\u002Fjobs-by-industry\u002Fasset-management). In the [insurance](\u002Fjobs-by-industry\u002Finsurance) sector, ML drives innovations in [P&C insurance](\u002Fjobs-by-expertise\u002Fpc-insurance), [reserving](\u002Fjobs-by-expertise\u002Freserving), and [loss modeling](\u002Fjobs-by-expertise\u002Floss-modeling), with demand concentrated in regions with strong insurance presences. The [investment management](\u002Fjobs-by-industry\u002Finvestment-management) industry relies on ML for [portfolio optimization](\u002Fjobs-by-expertise\u002Fportfolio-optimization), [catastrophe analysis](\u002Fjobs-by-expertise\u002Fcatastrophe-analysis), and [alternative risk transfer](\u002Fjobs-by-expertise\u002Falternative-risk-transfer), particularly in coastal financial districts. These industries use ML to enhance [financial modeling](\u002Fjobs-by-expertise\u002Ffinancial-modeling) and [market analysis](\u002Fjobs-by-expertise\u002Fmarket-analysis), supporting roles in [structured insurance](\u002Fjobs-by-expertise\u002Fstructured-insurance) and [underwriting support](\u002Fjobs-by-expertise\u002Funderwriting-support) across the [United States](\u002Fsearch).",{"title":682,"content":683},"In-Demand Skills & Employers","Leading U.S. employers like [Allianz Insurance](\u002Fcompany\u002Fallianz-insurance) and [Pacific Investment Mgt Co. LLC](\u002Fcompany\u002Fpacific-investment-mgt-co-llc) seek professionals with machine learning skills paired with domain expertise. Key areas include [risk assessment](\u002Fjobs-by-expertise\u002Frisk-assessment), [financial engineering](\u002Fjobs-by-expertise\u002Ffinancial-engineering), and [quantitative research](\u002Fjobs-by-expertise\u002Fquantitative-research), where ML enhances [actuarial science](\u002Fjobs-by-expertise\u002Factuarial-science) and [econometrics](\u002Fjobs-by-expertise\u002Feconometrics). Expertise in [client onboarding](\u002Fjobs-by-expertise\u002Fclient-onboarding), [relationship management](\u002Fjobs-by-expertise\u002Frelationship-management), and [key account management](\u002Fjobs-by-expertise\u002Fkey-account-management) is valuable for roles integrating ML into [sales](\u002Fjobs-by-expertise\u002Fsales) and [marketing activities](\u002Fjobs-by-expertise\u002Fmarketing-activities). ML supports [process improvement](\u002Fjobs-by-expertise\u002Fprocess-improvement) in [claims service planning](\u002Fjobs-by-expertise\u002Fclaims-service-planning) and [policy structuring](\u002Fjobs-by-expertise\u002Fpolicy-structuring), while in investment contexts, it aids [asset pricing](\u002Fjobs-by-expertise\u002Fasset-pricing) and [portfolio theory](\u002Fjobs-by-expertise\u002Fportfolio-theory). These employers also value skills in [client analytics](\u002Fjobs-by-expertise\u002Fclient-analytics) and [management reporting](\u002Fjobs-by-expertise\u002Fmanagement-reporting), using ML to derive insights from [financial markets](\u002Fjobs-by-expertise\u002Ffinancial-markets) data and drive [multinational business](\u002Fjobs-by-expertise\u002Fmultinational-business) strategies in the [United States](\u002Fsearch).",{"title":685,"content":686},"Career & Development","Career paths in machine learning within the U.S. financial sector often begin with roles in [data analysis](\u002Fjobs-by-skill\u002Fdata-analysis) or [financial analysis](\u002Fjobs-by-expertise\u002Ffinancial-analysis), progressing to specialized positions in [quantitative research](\u002Fjobs-by-expertise\u002Fquantitative-research) or [risk modeling](\u002Fjobs-by-expertise\u002Frisk-modeling). To build expertise, focus on mastering core IT skills like [Python](\u002Fjobs-by-skill\u002Fpython) for programming and [artificial intelligence](\u002Fjobs-by-skill\u002Fartificial-intelligence) tools, complemented by [Excel](\u002Fjobs-by-skill\u002Fms-excel) for data manipulation and [PowerPoint](\u002Fjobs-by-skill\u002Fms-powerpoint) for presenting insights. Learning [data science](\u002Fjobs-by-skill\u002Fdata-science) methodologies and using pricing tools or risk modeling software enhances capabilities in [financial modeling](\u002Fjobs-by-expertise\u002Ffinancial-modeling) and [investment analysis](\u002Fjobs-by-expertise\u002Finvestment-analysis). Relevant expertise includes [time series analysis](\u002Fjobs-by-expertise\u002Ftime-series-analysis) for [financial markets](\u002Fjobs-by-expertise\u002Ffinancial-markets) and [portfolio management](\u002Fjobs-by-expertise\u002Fportfolio-management) for [asset allocation](\u002Fjobs-by-expertise\u002Fasset-allocation). Professionals can advance by contributing to [research publication](\u002Fjobs-by-expertise\u002Fresearch-publication) or leading [business process improvement](\u002Fjobs-by-expertise\u002Fbusiness-process-improvement) initiatives, with opportunities in [actuarial science](\u002Fjobs-by-expertise\u002Factuarial-science) or [client analytics](\u002Fjobs-by-expertise\u002Fclient-analytics) across the [United States](\u002Fsearch).","skill:machine-learning:us","2026-08-17T09:02:32.814521+02:00",{"industry":690,"employment_type":726,"expertise_skills":735,"it_skills":796,"salary_currency":861,"processed_working_hours":865},[691,694,698,702,705,707,710,713,715,717,720,723],{"key":692,"label":463,"count":693},"financial services",24,{"key":695,"label":696,"count":697},"insurance","Insurance",19,{"key":699,"label":700,"count":701},"banking","Banking",10,{"key":703,"label":704,"count":29},"financial markets infrastructure","Financial Markets 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