Machine Learning Jobs in Jersey City, NJ

The role involves leading analytics delivery to clients, managing relationships, ensuring account growth, and solving complex business problems through data science in various sectors, primarily CPG and Retail.

Senior AI leader responsible for defining enterprise AI strategy, prioritizing high‑value use cases across underwriting, pricing, claims and operations, building an AI organization, partnering with technology/data/risk teams, ensuring regulatory compliance, and driving adoption and measurable business outcomes.

Lead document-vision research and production systems for multimodal legal evidence understanding. Define evaluation standards, frame hard problems, design and ship models, run efficacy studies and monitoring, mentor senior scientists, and advise leadership on scientific direction.

Develop, validate, deploy, and monitor AML/CTF and financial-crime models and analytics. Partner with business and engineering to translate problems into data science solutions, provide technical leadership, and support model performance, testing, and governance across enterprise initiatives.

Senior individual contributor responsible for leading pet/P&C insurance pricing model development (GLMs and ML), owning end-to-end projects, performing EDA and root-cause analysis, communicating results to stakeholders, and mentoring junior team members.

Lead design and delivery of secure, scalable AI/ML and generative/agentic solutions: develop LLMs and agent frameworks, productionize end-to-end models, mentor teams, collaborate with stakeholders, and drive R&D and PoCs to production.

The Director of Data Sciences leads a team in strategic initiatives, drives machine learning and AI solutions, and manages resources to enhance data-driven decision making across the business unit.

The role leads the design and delivery of AI-powered technology to enhance productivity in claims operations, managing cross-functional teams to achieve strategic objectives.

Senior-level engineer delivering analytics and ML-Ops solutions for healthcare payers. Design and implement analytics platforms on Databricks, develop CI/CD and provisioning patterns, collaborate with data scientists, mentor juniors, and build client relationships to drive actionable insights and operational improvements.

Entry-level role supporting data, analytics, technology and transformation projects. Participate in classroom training, complete assigned projects, follow coding and documentation best practices, build data-driven presentations, and analyze data to support decision-making while collaborating across Data-Analytics, Technology, and Transformation teams.

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.

Manage full sales cycle for enterprise accounts in the NYC/Mid-Atlantic region: hunt new logos, grow existing install base, build pipelines, meet quota, coordinate with BDR, marketing, and solutions architects, develop territory plans, forecast sales, build channel partnerships, and articulate/Leverage AI and cloud networking/security solutions to executive and technical stakeholders.

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 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 Principal Data Scientist will create scalable data solutions and machine learning models, collaborating with teams to drive innovation in the financial industry.

The Algorithmic Trading Senior Manager at FanDuel will lead the algorithmic trading team, focusing on developing automated trading strategies and optimizing capital utilization while managing risk and team operations.

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

Lead Data Science for Personal Lines (Homeowners, Auto) at Chubb. Oversee data engineers and scientists to develop pricing, underwriting, segmentation, lifetime value, and operational models. Drive AI adoption, partner with business leaders, shape hiring and upskilling, and deliver analytics products and insights to support strategic objectives.

Lead strategy and roadmap for Payments and Fraud platforms, manage product managers, drive improvements in payment success, fraud mitigation, routing, reconciliation, and real-time monitoring. Align stakeholders across Risk, Compliance, Finance, and Data Science while delivering measurable growth, reliability, and cost reductions.

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.

Summer data science internship working on ML projects (anomaly detection, numerical optimization, PII/PHI NLP). Interns will develop models, write high-quality Python, use Linux and development tools, work with git, Docker, CI/CD and Kubernetes, participate in code reviews and agile rituals, and present results to technical and non-technical audiences.

Develop and productionize AI features from PoC to scalable deployments. Write production-ready Python code, implement testing and debugging, refine user stories, participate in ethical and security reviews, and collaborate with stakeholders to align AI solutions with business needs.

Summer internship working on applied data science projects (e.g., anomaly detection, numerical optimization, PII/PHI NLP). Responsibilities include end-to-end model development, writing modular Python code, using Linux and development tools, containerization and deployment basics, participating in code reviews and agile rituals, and clearly presenting results to technical and non-technical audiences.

Lead end-to-end technical programs for ML infrastructure (inference, serving, efficiency, endpoints). Coordinate cross-functional teams, prioritize overlapping projects, manage timelines, budgets and scope, drive incident management and continuous improvement, and communicate status to engineering and leadership.

Develop prototypes, PoCs, and MVPs for GenAI solutions. Apply deep learning and ML principles using Python, Hugging Face, LangChain, OpenAI API, TensorFlow/Keras/PyTorch, and cloud model services. Work with multi-modal data and intelligent agent tools. Be self-motivated, collaborative, and focused on solving hard AI/GenAI problems.