Machine Learning Jobs in Jersey City, NJ

Lead client consulting workstreams focused on demand, supply, inventory, production, capacity planning, forecasting, and S&OP. Assess planning processes, design improvements, support APS and ERP implementations, develop analytics and AI-enabled planning solutions, and manage client relationships, deliverables, timelines, and junior team members. The role requires hands-on supply chain planning experience and collaboration with cross-functional stakeholders.

Lead the research, architecture, and production delivery of agentic and generative AI solutions for complex banking operations. Design collaborating agents, reusable services, evaluation frameworks, and distributed machine learning systems. Establish experimentation, monitoring, safety, reliability, and governance practices while partnering with business, product, and engineering teams. Mentor engineers and researchers and drive technical alignment across stakeholders.

Designs, deploys, maintains, and troubleshoots HPE NonStop payment infrastructure, secure key managers, Etinet servers, and related subsystems. Uses AIOps and AI-assisted practices for incident triage, anomaly detection, automation, documentation, and reliability improvements. Partners with SRE, observability, and cybersecurity teams to govern AI-enabled monitoring, maintain auditability, execute upgrades, and produce operational knowledge such as runbooks and troubleshooting guides.

Leads software engineering and quality engineering for JPMorganChase’s Payments team. Builds and maintains large-scale test automation frameworks, integrates automated testing into CI/CD pipelines, and supports application development, system design, and operational stability. Drives responsible use of AI-assisted engineering tools, improves test quality and delivery speed, and establishes validation, security, and resiliency standards. Oversees complex, high-risk projects and promotes practices such as failure triage, contract testing, service validation, and canary testing.

Leads post-trade operations for Fixed Income and Equities, overseeing clearance, balancing, trade matching, settlement, reconciliation, break management, controls, and risk escalation. Drives root-cause analysis, automation, process improvements, KPI and SLA governance, audit readiness, and regulatory compliance. Partners cross-functionally with Trading, Technology, Product, Risk, and Operations teams while managing strategic initiatives, communicating with senior stakeholders, coaching teams, and fostering operational excellence.

Own the roadmap and execution for W&B Models, an experiment-tracking platform for AI researchers. Improve experiment organization, training visualization, monitoring, evaluations, and large-scale analysis across web and mobile. Conduct user research, translate ML workflows into product decisions, collaborate with engineering and design, and use data and feedback to launch polished, high-performing experiences. The role requires deep familiarity with ML research, technical product management, data visualization, AI tools, and developer or researcher audiences.

Leads data governance and ownership initiatives, defining data requirements, quality standards, metadata, lineage, controls, and curated datasets. Performs SQL-based testing, validation, reconciliation, and root-cause analysis. Partners with technology and business teams to mitigate data risk, support analytics and machine learning use cases, and improve data products. Applies AI and LLM tools to automate governance activities and contributes to roadmap, agile delivery, and stakeholder management.

Own the analytics roadmap for a Consumer Banking customer pillar, leading measurement, experimentation, and analyses across growth, retention, pricing, servicing, and strategy. Translate complex business questions into executive-ready recommendations, influence cross-functional decisions, establish analytical standards, and mentor analytics talent. The role requires strong statistical, experimental, communication, visualization, and stakeholder-management skills.

Lead site reliability for liquidity risk applications, owning non-functional requirements. Drive resiliency, security, scalability, monitoring, automation, and enterprise-authorized AI adoption for reliability workflows. Influence strategy, run blameless post-mortems, coach engineers, hire and develop talent, and ensure traceability, auditability, and cross-team collaboration.

Lead data science and AI initiatives for Home Lending Operations by developing product analytics, machine learning, experimentation, KPI, and trigger-driven capabilities. Translate ambiguous business problems into actionable insights, improve client journeys and operational efficiency, and identify scalable agentic AI use cases. Partner with product, strategy, operations, and technology teams while communicating recommendations to senior stakeholders in a regulated environment.

Lead AI engineering teams to design, build, and deploy end-to-end AI solutions. Manage client relationships, plan and deliver projects, mentor staff, drive adoption of cloud-native microservices, CI/CD, vector databases and orchestration tools, and ensure high standards of quality, innovation, and compliance.

Lead teams applying advanced data science and machine learning to large-scale claims, clinical, and member data to generate actionable insights that reduce cost of care and improve population health. Manage client relationships, mentor staff, ensure project delivery and quality, develop payer-focused analytics products, present findings to senior stakeholders, and promote continuous improvement and knowledge sharing.

Maintains internal technical education content and coordinates recurring Lunch and Learns, engineering demos, and training sessions. Responsibilities include speaker scheduling, notes and recording publication, content audits, editorial updates, research, drafting, request tracking, and improving content discoverability. The role partners with engineers and subject matter experts to turn technical material into accessible learning resources and supports instructional design and curriculum development.

Supports AI strategy and GenAI adoption across Payments Sales by translating sales priorities into product requirements, technology initiatives, and actionable insights. Partners with sales, data, product, platform, CRM, and analytics teams to deliver AI-enabled use cases, improve sales lifecycle efficiency, and enhance pipeline visibility. Contributes to voice-of-sales activities, use-case prioritization, governance, stakeholder alignment, training, enablement, and monitoring of emerging AI trends.

Lead and contribute to full-stack Java/React development, design secure high-quality production code, drive adoption of AI-assisted engineering, improve automation and operational stability, evaluate vendor architectures, and lead engineering communities of practice while promoting resilient, secure CI/CD practices.

Lead data ownership and governance to deliver curated, reusable data products. Partner across business, technology, analytics, and risk to define metadata, lineage, quality rules, and lifecycle management. Drive data integration, mitigate data risks, enable analytics/ML use cases, automate governance with AI/LLMs, and manage delivery, remediation, and stakeholder relationships to ensure scalable, discoverable, and enterprise-fit data products.

Lead execution of a firmwide data product approach: define, develop, register, and govern reusable data products. Coordinate with business and CDAO teams, set KPIs/OKRs, produce strategic reports, advise data leaders, and drive adoption of best practices to enable analytics and AI use across the firm.

Lead cybersecurity architecture for cloud-native enterprise solutions: evaluate controls and new technologies, advise stakeholders, provide technical guidance, promote firmwide SDLC practices, and mentor peers to mitigate risk and adopt strategic solutions.

Design, build, and scale production-grade NLP and LLM-based solutions (search, summarization, classification, recommendations). Own end-to-end model lifecycle, partner with product/engineering/operations/risk, improve data quality and feedback loops, monitor models in production, and optimize workflows to reduce agent effort and improve consistency.

Lead technical delivery for analytics-focused software: design, develop, test, and operate secure, high-quality production code. Drive automation and CI/CD, evaluate vendor/architecture choices, mentor junior engineers, and promote resilient cloud-native solutions within financial services technology teams.

Lead design and implement scalable, secure ML and LLM-powered solutions on cloud. Produce production-quality Python code, build data pipelines and model inference services, drive AI governance and SDLC best practices, mentor teams, and collaborate cross-functionally to operationalize GenAI capabilities.

Design and implement enterprise cybersecurity architecture for cloud-native applications. Embed security across SDLC and CI/CD, build Python automation for monitoring and remediation, evaluate vendors, lead practice communities, and collaborate with risk and compliance to translate regulatory requirements into technical controls while validating AI-assisted security outputs.

Lead development of secure, high-quality production software for analytics and credit risk; design, code (Python/Java), review, troubleshoot, automate remediation, drive CI/CD and cloud-native solutions, mentor junior engineers, evaluate vendors, and promote new technologies.

Build and validate ML, optimization, and statistical models on large structured and unstructured consumer datasets; generate insights and actionable recommendations; communicate results to senior stakeholders; contribute across the full modeling lifecycle and advance team knowledge.

Lead architecture and delivery of production-grade agentic AI and NLP solutions for client onboarding. Design reusable frameworks (RAG, multi-agent orchestration, LLMs), integrate agents into enterprise systems, define safety/guardrails, advise stakeholders, and champion AI-assisted SDLC improvements to improve delivery, quality, and operational outcomes.