Bayesian Health

• Build and maintain the infrastructure for the Bayesian platform • Develop CI/CD to enable other team members such as software engineers, data scientists to accelerate their development • Propose enhancement to our infrastructure design to manage cloud costs and ensure reliability • Define a branching and promotion strategy that comply with the regulatory change control process • Build and maintain CI/CD pipelines using GitHub for automated testing and deployment • Create infrastructure guidelines and templates such as Terraform modules • Continuous monitoring of system performance and reliability, and apply software upgrades accordingly • Collaborate with team members in troubleshooting infrastructure issues and optimizing performance • Partner with SecOps engineer to implement security best practices complying with HIPAA, HITRUST, FDA, and client requirements • Architect and build a secure, internal AI Ops platform to host and manage AI/ML agents for infrastructure optimization.

• Design, develop, test, deploy, and maintain robust and scalable full-stack clinical AI modules in production. • Leverage AI coding tools (e.g., Claude Code, Cursor) to rapidly build an end-to-end application in production quality grade and AI agents to optimize testing, debugging, and production maintenance. • Partner with product managers, clinical, data scientists, and other software engineers to integrate our AI insights into intuitive and seamless clinical workflows. • Develop comprehensive and robust automated end-to-end tests to ensure platform and application functional integrity, meeting the requirements of enterprise health systems and regulatory.

• Develop and tune innovative, new ML models and labeler systems based on deep understanding of clinical use cases and state-of-the-art ML methods. • The same models that you develop with production-grade python. • Identify strategies for improving our production ML-based systems, and write, debug, and deploying production-grade Python code to implement those strategies. • Build infrastructure that enables ML model development and deployment in production systems.

• Support clinical study adjudication requirements • Review specific cases and provide clinical justification • Provide documentation of clinical determination

• Develop and implement a strategic sales and account growth plan for the region to exceed revenue goals and drive long-term growth • Build and nurture strong relationships with C-level executives, key stakeholders, and decision-makers within hospital systems • Lead, mentor, and develop a high-performing team; provide guidance on complex negotiations, consultative selling, and professional growth • Stay current with industry trends, market conditions, and competitor offerings to refine the go-to-market approach and position our technology effectively • Partner with Leadership, Product / Tech and Customer Success teams to ensure alignment on messaging, product feedback, and post-sale support

• Collaborate closely with clients to ensure successful integration of our solutions, providing exceptional support and guidance. • Conduct comprehensive chart reviews to identify opportunities for model refinement, potential enhancements to the product, and to gather success stories. • Build and nurture strong relationships with clinicians at client sites to facilitate seamless adoption and utilization of our technology. • Provide leadership and guidance to internal teams on clinical matters, leveraging your expertise to drive success. • Contribute to the development of presentations and participate in sales and marketing conversations, representing the nurse perspective, and demo product features. • Stay abreast of industry trends and advancements, contributing to the continuous improvement of our products and services. • Gain buy-in from nursing and physician stakeholders to drive adoption. • Support cross-functional team to build and deliver overarching narrative regarding value that Bayesian brings to each health system customer. • Support research and evaluation of Bayesian Health modules. • Identify gaps and barriers to achieving clinical outcomes and recommend countermeasures. • Contribute to the product roadmap by anticipating client needs. • Travel an average of 10% (including overnight) to client sites to provide support. • Be an integral part of our diverse, inclusive, collaborative, fully-remote Clinical team. • Other duties as assigned.

• The role of a Product Manager at Bayesian is to maximize value for our health system customers over a 1-year time horizon, ensuring our platform is an indispensable part of their standard of care every year. • Opportunity Assessment: review existing clinical literature and work with health system executives to understand their goals and validate a new module’s business case. • 0->1 PM: define the product vision, north star metrics, ROI drivers, and user stories with input from internal and external clinicians. • Rapid Prototyping: Use Cursor, Claude Code, or Codex CLI to build an interactive proof-of-concept. Iterate with end users to build trust and identify points of friction. • Product deployment: Partner with engineering, research, and the customer’s integration team to get the module deployed. Collaborate with research to define data requirements for model training and ground truth for model evals. Scope workflow requirements with the client and our engineering team. • 1-> n PM: The module is live and impacting patient care, now you must continue shipping to drive adoption and ROI. Analyze usage, debug process or institutional barriers to adoption, and continue speaking to end users to understand pain points. Guide the tech team and the client to ensure the module maximizes value for the customer.

• Set the technical vision and strategy for Bayesian Health's machine learning organization while building and leading a high-performing team of data scientists and ML engineers. • Partner deeply with Engineering to architect scalable data warehousing and ML infrastructure that enables rapid model development and reliable production deployment. • Roll up your sleeves on critical IC work: prototyping models, evaluating system performance, and debugging production issues. • Build, mentor, and scale a world-class AI/ML team, establishing technical standards, career development frameworks, and a culture of excellence and ownership. • Define and execute the ML roadmap while partnering closely with Engineering to architect data warehousing solutions, ML infrastructure, and data pipelines that enable the team to rapidly prototype and deploy models at scale. • Contribute directly to critical modeling, evaluation, and analysis work, ensuring the team ships high-quality ML systems that deliver measurable clinical impact. • Collaborate with Engineering, Product, and Clinical to translate complex clinical workflows into ML opportunities, and communicate model performance and impact to technical and non-technical stakeholders including customers and investors.

• Lead the integration of our clinical AI platform and application with our health system clients’ electronic health records (EHR). • Work with the client to understand the best integration strategy, and own the planning and execution of the project end-to-end. • Develop frameworks and tooling to increase efficiency and velocity of client implementations. • Create and execute testing plans to ensure smooth and timely integrations. • Partner with the Director of Engineering to define hiring strategy for future Forward Deployed Software Engineer hires. • Work closely with Client Success, Clinical, Product, and Data Science to drive alignment and deliver impactful technology that saves lives.

• Lead the development of clinical AI/ML product analytics infrastructure, frameworks, and tools to enable client success. • Partner with client success and clinical product subject matter experts to implement infrastructure, queries, and automation to monitor KPIs and success metrics. • Build frameworks and tools for clinical reviews and investigations of reported cases. • Propose and implement foundational improvements for data platform scalability and team analytics capabilities. • Drive product analytics development cross-functionally with various teams.