Machine Learning Jobs in Jersey City, NJ

Leads pricing strategy and profitability consulting engagements, analyzing pricing models, customer value, market trends, and business performance. Directs teams, manages stakeholders, budgets, and resources, and coaches consultants. Applies predictive analytics, machine learning, statistical modeling, and scenario analysis to develop actionable recommendations, improve client profitability, optimize customer value, and support strategic transformations.

Leads cybersecurity engineering teams developing enterprise security solutions across multiple technology domains. Clarifies security requirements, supports multi-level network security, performs threat modeling and vulnerability assessments, and advises leaders during security risks. Uses and validates enterprise-authorized AI for risk analysis, documentation, security testing, and control validation while ensuring data sensitivity, auditability, and resiliency. Coaches, develops, hires, and recognizes engineering talent.

Leads Oracle Cloud ERP and EPM finance transformation implementations, setting strategic direction, managing complex projects, developing client relationships, improving finance processes, and mentoring emerging leaders. The role requires extensive Oracle Cloud implementation experience, strong business process expertise, market development, client networking, presentation skills, and team leadership.

Leads architecture for international infrastructure platforms, shaping target-state designs, technical governance, technology evaluations, secure production code, and engineering standards. Provides technical direction across teams, reviews and debugs code, and influences product and operational decisions. Uses enterprise-authorized AI to accelerate architecture analysis, program planning, risk synthesis, governance, and reporting while validating outputs and protecting sensitive data. Requires strong software architecture, development, testing, operational stability, communication, and cross-regional technology experience.

Lead the architecture, development, deployment, and operation of enterprise AI/ML solutions for wholesale payments. Build production systems involving NLP, document AI, OCR, LLMs, RAG, and fine-tuning on AWS. Establish scalable data pipelines, MLOps, model governance, monitoring, and responsible AI practices. Partner with senior stakeholders, define roadmaps and success metrics, improve engineering standards, conduct technical reviews, and mentor engineers.

Develop and implement AI solutions and machine learning systems that enhance products and solve complex client problems. Responsibilities include advanced analytics, LLM production deployment, prompt engineering, RAG pipeline optimization, cross-functional collaboration, technical discovery, responsible AI advocacy, client relationship building, and mentoring junior team members.

Leads Fenergo product consulting engagements focused on AML, sanctions, data analytics, and regulatory risk solutions. Manages client accounts, oversees project planning and execution, guides analytics initiatives, and develops data-driven business strategies. Uses predictive analytics, machine learning, statistical modeling, and scenario analysis to generate insights. Manages and mentors teams, engages stakeholders, resolves conflicts, promotes innovation, and ensures high-quality deliverables and adherence to professional standards.

Leads pricing and revenue data science initiatives for consumer markets clients. Develops predictive models, forecasts trends and customer behavior, manages dashboards and visualizations, ensures data quality, and translates complex analytics into strategic decisions. Oversees client engagements, validates outcomes, directs complex projects, and coaches diverse, high-performing teams.

Leads architecture and engineering for consolidating global wide area networks into a unified platform. Defines carrier-scale routing, traffic engineering, resiliency, automation, telemetry, validation, and AI-assisted operational strategies. Oversees transformation plans, migrations, production rollouts, technical standards, risk controls, and reliability improvements while partnering across engineering, infrastructure, cloud, security, operations, and architecture teams.

Leads the analytics and artificial intelligence strategy for Auto Commercial Solutions, overseeing data scientists and quantitative analysts. The role develops machine learning, predictive analytics, generative AI, experimentation, governance, and scalable data products to improve dealer acquisition, relationship growth, sales effectiveness, portfolio performance, and commercial lending opportunities. It partners with senior leaders across product, technology, risk, finance, and sales while translating complex insights into business recommendations and building a high-performing analytics organization.

Lead cybersecurity strategy and architecture across products, applications, and technologies. Advise engineering teams, communicate complex risks to leadership, develop reusable security frameworks, create mitigation solutions, and evaluate emerging technologies. The role requires influencing cross-functional teams, shaping enterprise-wide architecture, and applying cloud-native, application, API, web, and mobile security expertise across a complex financial-services 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.

Leads technology risk and controls management within Identity and Access Management, including risk assessment, control governance, regulatory compliance, issue remediation, and executive reporting. Develops and matures Continuous Control Monitoring capabilities using automation, data analytics, AI, and machine learning to automate testing, detect exceptions, identify emerging risks, and improve control effectiveness. Partners with technology, data, product, business control, and regulatory stakeholders to strengthen the control environment.

Own the strategy, roadmap, and delivery of Applicant Security products addressing identity fraud, resume fraud, bot activity, and abuse. Partner with engineering, data science, cybersecurity, legal, HR, and vendors to define requirements, prioritize backlogs, manage releases, establish success metrics, and ensure privacy and compliance. Lead third-party security vendor evaluation and governance while balancing risk reduction with candidate experience in a complex enterprise environment.

Designs, prototypes, and scales secure agentic AI systems across enterprise Employee Platforms. The role architects custom agents, orchestration, memory, tool use, evaluation, and human-in-the-loop controls; implements AI solutions in public cloud environments; develops production code; establishes reusable architecture and governance patterns; and advises engineering, product, security, and platform teams on resilient, auditable AI-enabled solutions.

Leads engineering for a regulatory and financial reporting platform. Designs scalable architecture and infrastructure, develops secure Python or Java applications, applies AI-assisted development and agent workflows, and supports deployment, monitoring, reliability, and operational stability. Partners with technology, product, and reporting stakeholders on data flows, controls, integrations, and reporting solutions. Requires strong cloud, DevOps, SQL, data modeling, and regulatory reporting expertise.

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.

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.

Supports payer modernization and health services consulting engagements by analyzing operational challenges, optimizing processes, applying business analytics, engaging stakeholders, implementing quality standards, and guiding transformation initiatives. Develops operational support frameworks, conducts market research, manages change, and advises clients on improving service delivery and business performance while mentoring junior team members.

Leads and mentors multiple software engineering teams delivering secure, scalable financial technology. Establishes enterprise AI-assisted engineering and SDLC automation practices, improves delivery speed and quality, oversees technical execution, manages resources and budgets, ensures application resiliency and security, and collaborates with stakeholders. Requires hands-on software development, system design, testing, operational stability, and leadership experience using Java, Python, SQL, PostgreSQL, and AWS.

Lead design and build of a global AI/ML deployment platform: architect control plane, multi-region deployments, APIs, autoscaling, and integrations. Own technical direction, engage clients, drive governance of AI-assisted development, and ensure resilient, secure production-grade infrastructure.

Lead the architecture, development, deployment, and operation of enterprise AI/ML solutions for wholesale payments. Build production applications involving NLP, document AI, OCR, computer vision, LLMs, RAG, and fine-tuning on AWS. Establish scalable data pipelines, MLOps, model governance, monitoring, and responsible AI practices while mentoring engineers, setting technical standards, and aligning multi-quarter roadmaps with senior stakeholders.

Leads enterprise AI data risk management and governance across data quality, lineage, ownership, controls, transparency, and regulatory requirements. Partners with business, technology, risk, compliance, legal, privacy, model risk, and audit teams to assess AI data risks, close control gaps, establish governance standards, and support responsible AI adoption. Advises senior leaders, facilitates cross-functional decisions, and mentors analysts supporting AI governance and data management.

Develops and maintains prompts, responses, guidelines, and datasets for generative AI and large language model tools. Leads labeling initiatives with internal teams and third-party firms, trains stakeholders on LLM and dataset best practices, and supports quality assurance and testing. Requires native-level English, bilingual ability in a specified language, specialized domain knowledge, and a college degree or equivalent experience.

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.