Top AI & Machine Learning Jobs

Leads healthcare payer data science and modernization initiatives, developing predictive models, statistical analyses, and scenario-based insights from claims, reimbursement, utilization, and member experience data. Directs multiple analytical workstreams, advises executive clients, guides cross-functional teams, supports transformation roadmaps, and promotes responsible AI use. The role also shapes thought leadership, business growth, operational improvement, and delivery quality across payer advisory engagements.

Design and deploy AI systems and machine learning models, perform data wrangling and analysis, and integrate solutions into data infrastructure. Develop scalable AI applications, relational data systems, evaluation tools, automated tests, and monitoring capabilities. Assess AI behavior, robustness, bias, explainability, drift, hallucinations, and guardrails while evaluating technical controls and communicating findings to clients and stakeholders.

Leads multidisciplinary teams delivering AI-enabled, cloud-native applications on AWS. Oversees architecture, development, testing, deployment, modernization, platform engineering, security, reliability, and client advisory activities. Translates business objectives into technical roadmaps, guides complex engineering work, coordinates cross-functional architecture reviews, communicates delivery risks and options, and coaches teams across multiple workstreams.

Designs, builds, tests, and troubleshoots full-stack cloud-native applications on AWS. Translates client needs into technical solutions across architecture, development, testing, and delivery; builds scalable infrastructure and integration patterns; applies AI-enabled engineering methods; and supports modernization initiatives. The role also reviews technical work, mentors junior engineers, collaborates with architecture teams, and helps deliver cloud, data, and analytics solutions.

Leads AWS-native application design, development, testing, modernization, and architecture efforts. Manages technical workstreams, delivery timelines, scope, and team allocation while coaching engineers on cloud infrastructure, AI-native engineering, testing, and troubleshooting. Reviews technical designs and code, coordinates application and infrastructure activities, supports client architecture discussions, and improves delivery reliability and speed.

Analyzes complex data sets, develops Python-based data solutions and pipelines, and applies advanced analytics to support client strategy and business performance. Designs dashboards and visualizations, collaborates with cross-functional teams, manages stakeholders, improves data quality, and builds client relationships. The role also involves solving complex business challenges, identifying industry trends, and delivering high-quality work within PwC’s Risk & Regulatory practice.

Leads AI-enabled risk modeling services, setting strategy, managing client relationships, overseeing machine learning and predictive analytics solutions, and guiding end-to-end delivery. The role includes model governance, feature engineering, performance evaluation, innovation, cross-functional collaboration, team coaching, thought leadership, and business development across risk modeling engagements.

Analyze complex client problems using statistical modeling, exploratory analysis, data visualization, and machine learning. Develop AI and GenAI solutions, data workflows, pipelines, models, and quality checks that generate actionable business insights. Communate recommendations through documentation and visual storytelling, manage workstreams, collaborate with clients, and mentor junior team members.

Leads teams in designing and deploying scalable AI and machine learning solutions for consulting clients. Responsibilities include data wrangling, complex analysis, data modeling, AI system implementation, data pipeline and infrastructure oversight, deep learning, neural networks, data quality validation, and stakeholder collaboration. The manager coaches team members, manages performance, supports innovation, and applies Python, Java, and cloud technologies to build production-ready AI applications.

Lead and coach teams to design, develop, and deliver production-grade AI and data solutions for clients. Manage client accounts, ensure quality and timelines, analyze complex datasets, implement best practices, and drive continuous improvement while fostering client relationships and technical excellence.