Role Description The AI/ML Engineer, a full-stack AI/ML architect for the Revenue Cycle pillar, is responsible for both agentic solutions (UiPath Maestro, LLM orchestration, agent + bot + human-in-the-loop patterns) and predictive ML solutions. This role sets the AI architecture direction for Revenue Cycle, defines reference patterns, and collaborates with delivery teams to bring solutions from prototype to production. The ideal candidate is equally comfortable designing an agentic orchestration topology and shipping a production ML model. Key Responsibilities - Architect full-stack AI/ML solutions for Revenue Cycle, spanning agentic orchestration (UiPath Maestro, LLM agents, RPA bots, human-in-the-loop) and predictive ML. - Define reference architectures, design patterns, and guardrails for AI use in Revenue Cycle, in partnership with enterprise architecture, security, and data governance. - Lead design of high-complexity solutions such as agentic patient portal inbasket management, combining UiPath Maestro, AI agents, RPA bots, and human-in-the-loop oversight. - Develop, evaluate, and productionize ML models using Python, modern ML libraries, and Mayo Clinic's approved cloud and data platforms. - Partner with delivery leads, app analysts, and business analysts to translate Revenue Cycle opportunities into AI-enabled solutions with measurable ROI. - Establish standards for model lifecycle management, monitoring, bias and drift evaluation, and responsible AI practice aligned to Mayo Clinic policy. - Evaluate emerging AI capabilities (new LLM models, agentic frameworks, UiPath releases) and guide when and how to adopt them. - Mentor AI/ML engineers and developers across the program; contribute to enterprise AI community of practice. Benefits - Medical: Multiple plan options. - Dental: Delta Dental or reimbursement account for flexible coverage. - Vision: Affordable plan with national network. - Pre-Tax Savings: HSA and FSAs for eligible expenses. - Retirement: Competitive retirement package to secure your future. Equal Opportunity All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, gender identity, sexual orientation, national origin, protected veteran status or disability status. Learn more about the "EOE is the Law". Mayo Clinic participates in E-Verify and may provide the Social Security Administration and, if necessary, the Department of Homeland Security with information from each new employee's Form I-9 to confirm work authorization.

• Designing model evaluation pipelines for models in development and production • Designing user studies for subjective model evaluations. • Converting requirements into measurable metrics. • Designing and developing automated evaluation dashboard to see model performances and compare results. • Training new models to capture new and different evaluation metrics. • Communicating with the model team to help design better models based on the evaluation results. • Communicating with the data team to help decide the type of data necessary to improve model performance. • Communication with the product-manager to make sure product requirements are correctly measured. • Help grow the evaluation team as the founding member. • Lead the evaluation team in the future.

MIS - Analytics Engineer Virtual• United States Job Description: General Description: Phillips & Cohen Associates Ltd. is an award-winning probate estate and debt settlement industry leader. We are seeking an Analytics Engineer to support Strategy & Analytics function here at PCA and help identify opportunities that drive performance. The ideal candidate should demonstrate strategic curiosity, challenge assumptions and translate analysis into insights. This role combines analytics engineering, data modeling, automation, and applied analytics to transform data into actionable insights, connect data analysis to execution to accelerate growth and competitive advantage. The Analytics Engineer plays critical role in turning data into strategic asset – ensuring the business can rely on accurate, timely insights to drive performance and growth. Responsibilities: Support analytics use cases (segmentation, forecasting, A/B testing). Develop, implement and optimize debt recovery processes, including dialer, scoring and portfolio segmentation. Engineer new data assets and derived datasets when required information is not available in existing source systems. Develop scalable data models and schemas to support portfolio valuation, forecasting, reporting, and operational analytics. Eliminate conflicting metrics and definitions, creating a single source of truth across teams. Build and maintain automated end-to-end analytics workflows, including scheduling, orchestration, alerts, and reporting. Implement data quality rules, validation logic, automated QA checks, and monitoring frameworks to ensure data integrity. Perform exploratory data analysis to identify trends, data gaps, and metric behavior related to portfolio performance. Translate business questions and strategy needs into technical requirements, datasets, and analytical solutions. Create reports, dashboards, and analytical outputs that are usable and interpretable by non-technical stakeholders. Optimize SQL queries and ETL/ELT processes for performance, accuracy, and reliability. Collaborate closely with analytics, engineering, and business partners to align data definitions, metrics, and logic. Document data pipelines, models, transformations, business rules, and data dictionaries to support governance and continuity, ensuring consistent metric definition across the organization. Qualifications: Minimum of 3 years of experience in analytics engineering, data engineering, data analytics, or similar hybrid analytics roles. Advanced SQL skills with hands-on experience building scalable transformations and data models. Proficiency in programming language such as SAS, Python or R for analytics, automation, and modeling. Demonstrated experience designing datasets and data architecture to support advanced analytics and forecasting. Experience with BI and visualization tools such as Power BI, Tableau, or Looker. Strong analytical thinking with the ability to translate complex data into actionable business insights. Ability to work effectively in ambiguous, open-ended problem spaces. Understanding of statistical concepts and predictive modeling techniques. Strong written and verbal communication skills with both technical and non-technical audiences. Experience developing SQL Server jobs, stored procedures, triggers, or similar database objects. Familiarity with Git or version control systems. Background in non-performing debt industry, financial services, portfolio analytics, forecasting, or operations analytics is a plus.

• Design and deploy machine learning algorithms for use cases spanning e-commerce, consumer trends, markets, logistics, and new products • Work on real-world consumer data for natural language processing, image classification, time series analysis, outlier detection, user modeling, etc • Work with large unstructured data • Be at the intersection of mathematics, machine learning, business, and computer science and impact millions of users through your work • Multiply the effect of your data science and data team members by building frameworks, tools, and methodologies that the entire team use • Provide thought leadership to a team through high quality write-ups, reviews, and a strong vision grounded in practical experience and a wider industry view