Top AI & Machine Learning Jobs

Leads Johnson & Johnson’s global AI fluency strategy for commercial teams, including capability frameworks, role-based learning, adoption programs, governance, measurement, and responsible AI practices. The role drives enterprise transformation, workflow integration, executive alignment, investment prioritization, vendor relationships, and measurable business outcomes across global, regulated, matrixed functions.

Translate business objectives into actionable data analysis, analyze large datasets, apply statistical and machine learning methods, and clearly communicate findings to technical and non-technical audiences. The role requires Hadoop ecosystem expertise, Python scripting, strong statistical knowledge, sampling and analytical skills, and business domain experience in retail, enterprise mobility, or healthcare.

Build production-quality AI applications using Python and existing large language models. Design multi-agent and multi-step workflows with prompt chaining, tool use, and orchestration; engineer and evaluate prompts; integrate AI into RESTful APIs and microservices; and deploy solutions on AWS. Preferred work includes RAG pipelines, vector databases, observability, exploratory data analysis, and MLOps/LLMOps practices.

Develop and maintain the U.S. Census Bureau’s LLM Autocoder using Python, LLMs, prompt engineering, text classification, structured outputs, and testing. Build survey-specific pilots, troubleshoot production issues, evaluate model changes, and collaborate with data scientists and MLOps engineers on AWS deployment, rollback, and verification. Maintain documentation and support government handoff while meeting Census security and operational requirements.

Leads the strategy, architecture, delivery, governance, and sustainment of agentic AI, data products, dashboards, automations, and intelligent workflows. Establishes AI-ready data foundations, engineering standards, evaluation frameworks, CI/CD practices, security controls, monitoring, and responsible deployment processes. Owns programs end to end, partners with business stakeholders to drive adoption and value realization, and coaches technical talent. The role requires hands-on expertise in Python, SQL, enterprise data platforms, generative AI, LLM solutions, and regulated pharmaceutical environments.

Data Science Interns analyze and clean large datasets, create reports, dashboards, and visualizations, and support predictive modeling, statistical analysis, and machine learning initiatives. They identify trends, validate data quality, collaborate with business partners, and present insights and recommendations. The role offers hands-on experience with business data, mentoring, and analytics projects while requiring strong analytical, communication, and organizational skills.

Build and maintain data science models for a PRO loyalty program, including churn, retention, customer lifetime value, propensity, segmentation, reward redemption, and program economics. Design experiments, measure incrementality, identify at-risk members, and support loyalty and marketing decisions. Partner with Finance, Marketing, PRO, IT, and Data Engineering to translate analytical findings into actionable strategies, present results to stakeholders, and maintain reproducible model documentation.

Develop and deploy data analytics, machine learning, and AI applications. Responsibilities include analyzing complex datasets, building models, deploying containerized applications to Kubernetes, integrating LLM-powered features, monitoring and troubleshooting AI systems, and creating Streamlit prototypes. The intern will collaborate with data science teams and stay current with AI, data science, and cloud-computing practices.

Lead discovery and build end-to-end AI capabilities to automate high-leverage internal workflows and extend intelligence to physician and employer experiences. Ship production LLM-based features with eval frameworks, partner on data and architecture, and create reusable patterns to compound product value.

Leads AI engineering strategy and a high-performing team developing scalable, secure, and responsible AI solutions. Drives LLM-powered decision automation, fraud detection, process optimization, model evaluation, monitoring, governance, A/B testing, backtesting, and drift detection. Partners with product, UX, and engineering stakeholders to translate business priorities into AI roadmaps and production capabilities. The role requires extensive software, machine learning, or data engineering experience and strong technical leadership.