Machine Learning Jobs in Jersey City, NJ

Lead the design and development of high-performance trading systems using KDB/q, Python, AI, and machine learning. Build quantitative models, time-series analytics, research and backtesting frameworks, and real-time or batch production pipelines. Optimize systems for scalability, stability, and performance while collaborating with quants, traders, product owners, and engineers. Mentor developers, support production systems, and participate in on-call rotations.

Leads operational support for production AI solutions, including monitoring, incident management, defect remediation, enhancements, deployments, governance, and performance evaluation. Coordinates internal teams and third-party AI engineers, maintains support processes and documentation, and monitors reliability, accuracy, drift, bias, fairness, cost, and business value. Partners with data, technology, governance, and business stakeholders to ensure AI solutions remain stable, compliant, effective, and aligned with evolving needs.

The postdoctoral associate will design systems for transactive energy communities, focusing on socio-technical energy behavior and algorithm implementation for mechatronic systems.

Leads the design, architecture, and delivery of AI-enabled applications and digital initiatives. Evaluates generative AI, machine learning, robotic process automation, intelligent data processing, and predictive analytics use cases. Manages project scope, budgets, schedules, resources, quality, SLAs, and stakeholders. Translates business needs into technical solutions, drives operational excellence, and mentors teams on emerging AI technologies across financial services.

Build, deploy, and operate production AI applications and scalable machine learning solutions. Develop automated training and deployment workflows, support production AI infrastructure, and collaborate with software, data science, and platform engineering teams. The role involves Python development, ML frameworks, containerization, Kubernetes, Linux, and Git, with preferred experience in LLMs, RAG pipelines, model serving, vector databases, frontend development, and ML orchestration.

Provide commercial legal support for enterprise cloud, SaaS, and infrastructure transactions. Draft, negotiate, and advise on contracts (SaaS, IaaS, software licensing, DPA), counsel sales and cross-functional teams on legal/privacy/commercial risk, manage concurrent deals, improve templates and processes, and coordinate outside counsel to enable scalable, business-focused legal support for growth.

Lead supply chain analytics consulting engagements, offshore teams, and client discussions. Translate supply chain business challenges into scalable data and analytics solutions using Python, SQL, machine learning, AI, optimization, and operations research. Provide domain expertise across demand and supply planning, shipping, and inventory management; mentor analysts and data professionals; architect analytics frameworks; and communicate actionable insights to business stakeholders.

Leads and builds the pharmaceutical Business Intelligence and Analytics function, partnering with commercial leadership and cross-functional teams. Owns data strategy, integration, reporting, dashboards, forecasting models, market research, patient-finding analytics, omnichannel measurement, field-force reporting, and business development assessments. Uses SQL Server, Excel, Tableau or Power BI, advanced analytics, AI, and machine learning to generate actionable insights, improve patient access, and support commercial decisions.

Drive AI/ML solution sales for cable, media, and entertainment clients by developing POCs, proposing solutions, advising customers, building roadmaps, coordinating offshore delivery teams, and performing data-driven analysis to identify business improvements and implementation plans.

Data Science interns apply analytical and data science methods to business and engineering problems. Responsibilities include data profiling, feature engineering, identifying patterns and anomalies, building analytical models and automated tools, and partnering with engineering and business teams to deliver actionable recommendations. Interns gain experience with programming, advanced analytics, machine learning, distributed computing, and scalable data-driven solutions through a structured 10-week program.

The Senior Product Manager will define the product strategy and roadmap for AI and data products, lead product initiatives, and collaborate with engineering and data science teams. The role requires transforming complex problems into actionable products that enhance healthcare decision-making.

The Solutions Engineer designs and delivers technical solutions for customer deployments, integrations, and implementations. Responsibilities include gathering requirements, integrating APIs and SFTP solutions, conducting demos and proofs of concept, creating technical documentation, handling security questionnaires, and ensuring projects meet scope, budget, and timelines. The role partners with sales, product, solution architecture, and regional teams while maintaining customer satisfaction and identifying innovative use cases.

Lead engineer responsible for deploying and serving end-to-end ML models and cloud-native services, driving AI-assisted engineering practices, producing secure, high-quality production code, influencing product design and operations, and mentoring technical teams.

Lead end-to-end development and production deployment of causal and predictive ML solutions for private banking. Frame causal problems, build uplift/HTE models and observational causal studies, drive MLOps, monitor model quality, mentor data scientists, and translate causal/LLM research into production in partnership with risk, compliance, and business stakeholders.

Lead design and delivery of agentic and generative AI systems for banking: translate business problems into research and engineering plans, build multi-agent solutions, design reusable services and evaluation practices, mentor researchers/engineers, and partner to scale secure, reliable production deployments.

The AI Engineer will analyze business processes for automation opportunities, design and maintain RPA solutions, and develop Intelligent Document Processing systems while collaborating with stakeholders and ensuring reliability of solutions.

The AI Executive Partner will advise C-Suite executives on leveraging AI to drive business outcomes, manage member relationships, and deliver innovative AI solutions.

Leads application development strategy, architecture, engineering delivery, production support, and technical innovation for the Client Tax team. Designs and maintains full-stack, cloud-native applications using Java, React, Oracle, Kafka, Snowflake, microservices, and AI technologies. Establishes engineering standards, mentors engineers, drives reuse, and collaborates across business, product, operations, engineering, and security teams to deliver scalable, compliant solutions.

Lead design, build, and operate cloud-native AI/ML platform infrastructure and shared services. Architect scalable, secure GPU-enabled training and inference environments, implement IaC and automation, support model deployment/serving pipelines, drive observability and CI/CD, and promote responsible AI practices and enterprise AI-assisted development across teams.

Leads specialty exposure management for non-natural catastrophe and liability portfolios across insurance lines. Oversees analysts performing pre- and post-bind modeling, portfolio production, reporting, regulatory submissions, data quality, and model validation. Partners with underwriting teams to optimize risk-adjusted capacity deployment, presents portfolio insights to senior stakeholders, manages outsourcing relationships, and advances exposure management tools, workflows, AI, automation, and analytics capabilities. Requires extensive insurance or reinsurance catastrophe modeling experience and strong leadership, risk governance, SQL, and analytical skills.

Build predictive and cohort models supporting underwriting and marketing. Source, evaluate, combine, and analyze internal and external data; develop analytical datasets and solutions; implement and monitor models in production; communicate insights to business partners; document methodologies and results; and support statistical analysis requests. May supervise 1–2 analysts and provide guidance on cohort modeling and related analytics techniques.

Lead and manage a data science team building production-ready machine learning models to predict sports outcomes, price markets, create dashboards, and drive insights. Collaborate with product, risk, and trading teams; oversee data collection and cleaning; apply Bayesian methods; troubleshoot code; present to senior leaders; hire and develop talent; improve processes and automation.

Develop reusable ML tooling and libraries, collaborate with data scientists to productionize models, build services and APIs (Docker/Linux), follow engineering best practices, and support project lifecycle from intake to maintenance.

The VP, Data Science will lead analytics initiatives in pricing modeling and risk assessment for commercial insurance, mentoring teams and ensuring regulatory compliance.

Lead and scale Mimica's product organisation, define global product strategy and roadmap for AI-driven task/process mining and automation, mentor product and design teams, improve product operations and discovery, align cross-functional stakeholders, handle escalations, and hire and forecast team needs.