[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"seo-content-skill-machine-learning-us":3,"job-search-\u002Fde-us\u002Fjobs-by-skill\u002Fmachine-learning?page=2":33,"skill-page-filters-\u002Fde-us\u002Fjobs-by-skill\u002Fmachine-learning?page=2":689,"featured-jobs-\u002Fde-us\u002Fjobs-by-skill\u002Fmachine-learning?page=2":867},{"image_path":4,"content_de":5,"content_en":18,"composite_key":31,"last_updated":32},"\u002Fimages\u002Fskill\u002Fmachine-learning.webp",{"tab1":6,"tab2":9,"tab3":12,"tab4":15},{"title":7,"content":8},"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":10,"content":11},"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":13,"content":14},"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":16,"content":17},"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":19,"tab2":22,"tab3":25,"tab4":28},{"title":20,"content":21},"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":23,"content":24},"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":26,"content":27},"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":29,"content":30},"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",{"total":34,"jobs":35},51,[36,102,129,168,203,230,279,312,335,353,384,420,460,495,521,548,585,612,639,674],{"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":75,"processed_job_expertise_skills_labels":76,"max_cpc":100,"actual_cpc":101},76886,"data-architect-federal-reserve-bank-of-st-louis","Data Architect",null,false,{"name":43,"slug":44,"logo_url":40},"Federal Reserve Bank of St. Louis","federal-reserve-bank-of-st-louis",[46],"St. Louis",[48],"US","onsite",140000,170000,"USD","estimated",[55],"FULL_TIME","permanent",{"ai":58,"aws":59,"sql":60,"postgresql":60,"machine learning":58,"data modeling tools":60,"cloud data platforms":60,"big data technologies":59,"data catalog platforms":59,"data warehousing tools":59,"distributed query engines":59,"metadata management platforms":59,"large-scale data processing systems":59},2,3,4,[62,63,64,65,66,67,68,69,70,71,72,73,74],"PostgreSQL","SQL","AWS","Cloud Data Platforms","Data Modeling Tools","Data Warehousing Tools","Big Data Technologies","Machine Learning","AI","Data Catalog Platforms","Metadata Management Platforms","Distributed Query Engines","Large-scale Data Processing Systems",{"caching":60,"dataops":60,"encryption":60,"auditability":60,"data security":60,"access control":60,"data governance":60,"database design":60,"data engineering":60,"time-series data":59,"data architecture":60,"economic datasets":58,"public data access":58,"retention policies":60,"analytical datasets":59,"data classification":60,"data access patterns":60,"partitioning strategies":60,"data privacy regulations":60,"data integration patterns":60,"data lifecycle management":60,"data storage technologies":60,"research data environments":58},[77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99],"Data Architecture","Data Engineering","Database Design","Data Governance","Data Security","DataOps","Data Privacy Regulations","Data Lifecycle Management","Data Integration Patterns","Data Storage Technologies","Partitioning Strategies","Caching","Data Access Patterns","Data Classification","Access Control","Encryption","Retention Policies","Auditability","Time-series Data","Analytical Datasets","Economic Datasets","Research Data Environments","Public Data Access",1,1.5,{"id":103,"slug":104,"title":105,"raw_title":40,"is_featured":41,"company":106,"raw_hiringOrganization_logo_url":40,"processed_city_gmaps":109,"processed_country_iso_code_gmaps":111,"processed_home_office":49,"processed_salary_min":112,"processed_salary_max":112,"processed_salary_currency":52,"processed_salary_source":53,"processed_working_hours":113,"processed_employment_types":114,"processed_it_skills":115,"processed_it_skills_labels":116,"processed_job_expertise_skills":122,"processed_job_expertise_skills_labels":123,"max_cpc":100,"actual_cpc":101},73287,"catastrophe-research-development-intern-tigerrisk-partners-llc","Catastrophe Research & Development Intern",{"name":107,"slug":108,"logo_url":40},"TigerRisk Partners LLC","tigerrisk-partners-llc",[110],"Edina",[48],52000,[55],"student",{"word":58,"excel":59,"powerpoint":58,"machine learning":58,"data visualization":58,"artificial intelligence":58},[117,118,119,69,120,121],"Excel","Word","PowerPoint","Artificial Intelligence","Data Visualization",{"research":59,"data analysis":59,"risk assessment":58,"report preparation":58,"catastrophe modeling":58},[124,125,126,127,128],"Catastrophe Modeling","Data Analysis","Risk Assessment","Research","Report Preparation",{"id":130,"slug":131,"title":132,"raw_title":40,"is_featured":41,"company":133,"raw_hiringOrganization_logo_url":40,"processed_city_gmaps":137,"processed_country_iso_code_gmaps":139,"processed_home_office":140,"processed_salary_min":141,"processed_salary_max":142,"processed_salary_currency":52,"processed_salary_source":53,"processed_working_hours":143,"processed_employment_types":56,"processed_it_skills":144,"processed_it_skills_labels":145,"processed_job_expertise_skills":150,"processed_job_expertise_skills_labels":151,"max_cpc":100,"actual_cpc":101},71239,"senior-accountant-ii-allianz-insurance-2","Senior Accountant II",{"name":134,"slug":135,"logo_url":136},"Allianz Insurance","allianz-insurance","https:\u002F\u002Fcdn.phenompeople.com\u002FCareerConnectResources\u002FAISAIPGB\u002Fimages\u002FHeader-1706868786965.svg",[138],"Chicago",[48],"hybrid",90000,110000,[55],{"ai":59,"excel":60,"alteryx":59,"chatgpt":59,"powerpoint":60,"machine learning":58,"microsoft copilot":59,"sap analytics cloud":59},[117,119,146,147,148,149,70,69],"Alteryx","SAP Analytics Cloud","Microsoft Copilot","ChatGPT",{"tax":59,"gaap":60,"ifrs":60,"audit":59,"accounting":60,"reinsurance":60,"data analysis":60,"p&c insurance":60,"journal entries":60,"asset management":59,"project management":59,"process improvement":59,"statutory reporting":60,"financial statements":60,"regulatory compliance":60,"account reconciliation":60,"intercompany transactions":60},[152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,125],"Accounting","GAAP","IFRS","Statutory Reporting","Reinsurance","P&C Insurance","Asset Management","Financial Statements","Regulatory Compliance","Tax","Audit","Project Management","Process Improvement","Journal Entries","Account Reconciliation","Intercompany Transactions",{"id":169,"slug":170,"title":171,"raw_title":40,"is_featured":41,"company":172,"raw_hiringOrganization_logo_url":40,"processed_city_gmaps":175,"processed_country_iso_code_gmaps":177,"processed_home_office":178,"processed_salary_min":179,"processed_salary_max":179,"processed_salary_currency":52,"processed_salary_source":53,"processed_working_hours":180,"processed_employment_types":56,"processed_it_skills":181,"processed_it_skills_labels":182,"processed_job_expertise_skills":188,"processed_job_expertise_skills_labels":189,"max_cpc":100,"actual_cpc":101},80125,"product-manager-senior-expert-allstate-insurance-co","Product Manager Senior Expert",{"name":173,"slug":174,"logo_url":40},"Allstate Insurance Co.","allstate-insurance-co",[176],"Northbrook",[48],"remote",235000,[55],{"sql":59,"python":59,"adtech platforms":60,"machine learning":59,"data science tools":59,"demand side platforms":60,"customer data repositories":59},[63,183,184,69,185,186,187],"Python","Data Science Tools","AdTech Platforms","Demand Side Platforms","Customer Data Repositories",{"adtech":60,"user research":59,"product strategy":60,"budget monitoring":59,"product management":60,"roadmap development":60,"mobility data analysis":60,"omnichannel advertising":60,"platform monitoring tools":59,"build-buy-partner decisions":60,"external partner management":60,"product lifecycle management":60,"kpi definition and management":60},[190,191,192,193,194,195,196,197,198,199,200,201,202],"Product Management","Product Strategy","Product Lifecycle Management","KPI Definition and Management","Budget Monitoring","AdTech","Mobility Data Analysis","Omnichannel Advertising","User Research","Roadmap Development","Build-Buy-Partner Decisions","External Partner Management","Platform Monitoring Tools",{"id":204,"slug":205,"title":206,"raw_title":40,"is_featured":41,"company":207,"raw_hiringOrganization_logo_url":40,"processed_city_gmaps":210,"processed_country_iso_code_gmaps":212,"processed_home_office":49,"processed_salary_min":213,"processed_salary_max":214,"processed_salary_currency":52,"processed_salary_source":53,"processed_working_hours":215,"processed_employment_types":114,"processed_it_skills":216,"processed_it_skills_labels":217,"processed_job_expertise_skills":223,"processed_job_expertise_skills_labels":224,"max_cpc":100,"actual_cpc":101},68784,"intelligent-automation-developer-internship-summer-2027-auto-owners-insurance-company","Intelligent Automation Developer Internship - Summer 2027",{"name":208,"slug":209,"logo_url":40},"Auto-Owners Insurance Company","auto-owners-insurance-company",[211],"Lansing",[48],35000,45000,[55],{"nlp":58,"rpa":58,"java":58,"agile":58,"ocr\u002Ficr":58,"data analysis":58,"machine learning":58},[218,69,219,220,125,221,222],"RPA","NLP","OCR\u002FICR","Java","Agile",{"code review":58,"documentation":58,"system analysis":58,"software testing":58,"software development":58},[225,226,227,228,229],"Software Development","Software Testing","Documentation","Code Review","System Analysis",{"id":231,"slug":232,"title":233,"raw_title":40,"is_featured":41,"company":234,"raw_hiringOrganization_logo_url":40,"processed_city_gmaps":237,"processed_country_iso_code_gmaps":239,"processed_home_office":49,"processed_salary_min":240,"processed_salary_max":241,"processed_salary_currency":52,"processed_salary_source":53,"processed_working_hours":242,"processed_employment_types":56,"processed_it_skills":243,"processed_it_skills_labels":244,"processed_job_expertise_skills":265,"processed_job_expertise_skills_labels":266,"max_cpc":100,"actual_cpc":101},66326,"data-engineering-amp-platform-enablement-practice-lead-us-td-bank-na","Data Engineering & Platform Enablement Practice Lead (US)",{"name":235,"slug":236,"logo_url":40},"TD Bank, N.A.","td-bank-na",[238],"Mount Laurel",[48],123680,200200,[55],{"ai":59,"api":60,"aws":60,"sql":60,"azure":60,"kafka":59,"python":60,"dataops":60,"etl\u002Felt":60,"data lake":60,"snowflake":59,"automation":60,"databricks":60,"data fabric":60,"data quality":60,"google cloud":60,"data modeling":60,"data security":60,"observability":60,"data streaming":60,"data governance":60,"data integration":60,"data warehousing":60,"machine learning":59,"event-driven architecture":60},[245,246,64,247,248,249,250,251,252,253,82,254,255,63,183,256,257,258,259,260,261,262,263,264,70],"Databricks","Azure","Google Cloud","Kafka","Snowflake","ETL\u002FELT","API","Event-driven architecture","Data streaming","Observability","Automation","Data modeling","Data governance","Data security","Data quality","Data integration","Data warehousing","Data lake","Data fabric","Machine learning",{"itil":59,"budgeting":59,"compliance":60,"data security":60,"data 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