Role Overview

We are seeking a Sr. Technical Product Manager to lead the strategy and execution of data product, AI/ML system, AI-powered tooling, and automation initiatives across the go-to-market and operational teams embedded within Natera’s core business units (i.e., Oncology, Women’s Health, Organ Health), including Sales, Marketing, and Medical stakeholders. 

This role focuses on building, scaling, and leveraging platforms and products that power decision intelligence and activity orchestration across these domains. You will work in close collaboration with ‘S&M + GM’ leaders while building through centralized Data & AI organization platforms, standards, and governance. You will own the full product lifecycle from discovery through production, ensuring solutions are adopted, trusted, and deliver measurable business impact. In doing so, you will work hands-on as an empowered builder of AI and software solutions to improve workflow efficiency, productivity, and quality for the teams you support.

This is a technical product role requiring fluency in data systems, modern data platforms, ML, and AI implementation patterns (and the ability and drive to build with them directly), combined with strong experience and stakeholder intuition across Sales, Marketing, Medical, and related functions.

This role sits at the intersection of business impact and technical depth, with deep visibility into commercial performance and executive decision-making. You will have direct ownership of high-impact initiatives fueling overall organizational success.

What You’ll Do

Strategy & Roadmap

• Define and own the Data & AI product strategy and roadmap for the S&M + GM pod by deeply partnering with business leaders to proactively identify high-impact opportunities, shape problem definitions, and drive aligned priorities
• Translate ambiguous business problems (e.g., churn risk, campaign performance, clinical profile segmentation, next-best-action orchestration) into clear product direction and measurable outcomes
• Own the line between ‘what the business needs’ and ‘what gets built’ end-to-end

Discovery, Experimentation, & Requirements

• Develop deep context on the domain so you can proactively propose solutions rather than field requests
• Be hands-on with data: query datasets, review schemas, and validate assumptions through analysis
• Lead end-to-end product discovery with interviews, workflow mapping, data assessments, ROI modeling, etc.
• Define clear product requirements (PRDs, user stories, acceptance criteria) and success metrics
• Design and run experiments to validate product performance and measure causal impact
• Establish leading indicators and KPIs for proactive product and process health assessments

Delivery, Data, & ML Lifecycle

• Partner with data and AI/ML engineering resources to deliver scalable products and capabilities
• Guide development of robust data pipelines and unified data models (360° views across key entities)
• Own the end-to-end ML lifecycle: feature definition, evaluation, deployment, monitoring, drift detection, and retraining
• Ensure training–serving consistency, model versioning, and clear deployment decision gates
• Establish strong observability across data pipelines and models (data quality, latency, reliability, cost)

AI Productization & Hands-On Building

• Define and implement AI product patterns, including agentic workflows and tool/function-calling integrations
• Build process automations and internal tools that improve workflow efficiency, productivity, and output quality for the teams you support
• Establish evaluation frameworks for LLM-powered features (faithfulness, relevance, safety, cost, latency)
• Implement prompt strategies, guardrails, and continuous evaluation pipelines

Governance, Compliance, & Data Quality

• Ensure products meet regulatory and compliance requirements
• Champion data quality, lineage, and reliability through data contracts and observability standards
• Maintain strong documentation practices (e.g., model cards, dataset documentation, audit trails)
• Partner with governance teams (Security, Legal, Compliance, AI Governance) to operationalize AI responsibly

Adoption, Change Management, & Impact

• Launch products with supporting enablement activities to ensure solutions are embedded in workflows with confidence
• Partner with stakeholders to integrate products into proactive, day-to-day decision-making
• Monitor product usage, performance, and business outcomes to iterate based on data and feedback
• Quantify and communicate impact (e.g., revenue lift, cost reduction, cycle time improvements, forecasting accuracy)
• Influence across Sales, Marketing, Medical, and GM stakeholders through delivery, not authority

Qualifications

Required

• 7+ years of relevant experience (e.g., product management, applied AI/ML engineering, technical founder, forward-deployed engineering), including 3+ years building data products, AI/ML systems, or related platforms
• Demonstrated domain fluency in Sales, Marketing, Field Medical, and/or related commercial/GTM functions 
• Strong understanding of sales funnel, marketing funnel, medical affairs funnel, and related funnels to relate product ideas to the overall opportunity landscape
• Familiarity with key domain-relevant tooling (e.g., Salesforce) and associated data 
• Strong technical capability (e.g., comfortable scripting, performing EDA, with AI-aided application development)
• Hands-on experience shipping LLM-powered features or automations (e.g., prompting, RAG, tool/function calling, agentic orchestration, evals)
• ML/MLOps literacy (evaluation metrics, experimentation, feature stores, CI/CD, monitoring, drift)
• Familiarity with modern data stacks (e.g., Snowflake, dbt, AWS/SageMaker, Airflow/Dagster)
• Strong stakeholder leadership and communication skills, with the ability to work across technical and business teams
• Experience working in regulated environments (e.g., healthcare, life sciences, fintech)

Preferred

• Experience in healthcare, diagnostics, life sciences, and/or digital health
• Familiarity with healthcare data domains and systems
• Experience with vendor evaluation, platform selection, or external AI tooling ecosystems
• Advanced degree in a technical and/or business discipline

What Success Looks Like

• Production-grade data and AI products are deployed and embedded in day-to-day decision-making workflows
• Process automations and internal tools you build are reducing manual effort and improving output quality across the teams you support
• Measurable improvements seen in key business metrics (e.g., increased sales efficiency, better campaign performance)
• Reliable, high-quality data foundations connect patient, clinical, financial, and commercial insights end-to-end
• AI systems are continuously monitored, evaluated, and improved with measurable business impact
• Stakeholders trust and adopt implemented solutions across both corporate and business-unit teams
• Internal teams have transparent access to the function’s roadmap, trust in delivery against it, and visibility into the evaluation logic that prioritizes initiatives based on their impact to organizational goals

Role Overview

We are seeking a Sr. Technical Product Manager to lead the strategy and execution of data product, AI/ML system, AI-powered tooling, and automation initiatives across Patient Experience and Commercial Operations functions. This role is focused on improving how patients navigate testing and billing while enabling operational teams to efficiently progress cases from order to completion. You will play a crucial part in supporting initiatives to reduce friction, improve patient trust, and increase operational efficiency, ultimately driving better conversion, collections, and experience.

This role focuses on building, scaling, and leveraging platforms and products that power decision intelligence and activity orchestration across these domains. You will work in close collaboration with domain leaders while building through centralized Data & AI organization platforms, standards, and governance. You will own the full product lifecycle from discovery through production, ensuring solutions are adopted, trusted, and deliver measurable business impact. In doing so, you will work hands-on as an empowered builder of AI and software solutions to improve workflow efficiency, productivity, and quality for the teams you support.

This is a technical product role requiring fluency in data systems, modern data platforms, ML, and AI implementation patterns (and the ability and drive to build with them directly), combined with strong experience and stakeholder intuition across the associated functions. The role has direct ownership of high-impact initiatives that deeply influence organizational success.

What You’ll Do

Strategy & Roadmap

• Define and own the Data & AI product strategy and roadmap for the Patient Experience + Commercial Operations pod by deeply partnering with business leaders to proactively identify high-impact opportunities, shape problem definitions, and drive aligned priorities
• Translate ambiguous business problems (e.g., stuck cases, fragmented patient communication, support inefficiencies, billing friction) into clear product direction and measurable outcomes
• Own the line between 'what the business needs' and 'what gets built' end-to-end

Discovery, Experimentation, & Requirements

• Develop deep context on the domain so you can proactively propose solutions rather than field requests
• Be hands-on with data: query datasets, review schemas, and validate assumptions through analysis
• Lead end-to-end product discovery with interviews, workflow mapping, data assessments, ROI modeling, etc.
• Define clear product requirements (PRDs, user stories, acceptance criteria) and success metrics
• Design and run experiments to validate product performance and measure causal impact
• Establish leading indicators and KPIs for proactive product and process health assessments

Delivery, Data, & ML Lifecycle

• Partner with data and AI/ML engineering resources to deliver scalable products and capabilities
• Guide development of robust data pipelines and unified data models (360° views across key entities)
• Own the end-to-end ML lifecycle: feature definition, evaluation, deployment, monitoring, drift detection, and retraining
• Ensure training–serving consistency, model versioning, and clear deployment decision gates
• Establish strong observability across data pipelines and models (data quality, latency, reliability, cost)

AI Productization & Hands-On Building

• Define and implement AI product patterns, including agentic workflows and tool/function-calling integrations
• Build process automations and internal tools that improve workflow efficiency, productivity, and output quality for the teams you support
• Establish evaluation frameworks for LLM-powered features (faithfulness, relevance, safety, cost, latency)
• Implement prompt strategies, guardrails, and continuous evaluation pipelines

Governance, Compliance, & Data Quality

• Ensure products meet regulatory and compliance requirements
• Champion data quality, lineage, and reliability through data contracts and observability standards
• Maintain strong documentation practices (e.g., model cards, dataset documentation, audit trails)
• Partner with governance teams (Security, Legal, Compliance, AI Governance) to operationalize AI responsibly

Adoption, Change Management, & Impact

• Launch products with supporting enablement activities to ensure solutions are embedded in workflows with confidence
• Partner with stakeholders to integrate products into proactive, day-to-day decision-making
• Monitor product usage, performance, and business outcomes to iterate based on data and feedback
• Quantify and communicate impact (e.g., revenue lift, cost reduction, cycle time improvements, case completion turnaround)
• Influence Patient Experience and Commercial Operations stakeholders through delivery, not authority

Qualifications

Required

• 7+ years of relevant experience (e.g., product management, applied AI/ML engineering, technical founder, forward-deployed engineering), including 3+ years building data products, AI/ML systems, or related platforms
• Demonstrated domain fluency in Patient Experience and/or Commercial Operations
• Strong understanding of patient journeys, support channels (e.g., chat, voice, call centers), and operational workflows that drive conversion and test completion
• Familiarity with key domain-relevant tooling (e.g., patient portals, support platforms) and associated data
• Strong technical capability (e.g., comfortable scripting, performing EDA, with AI-aided application development)
• Hands-on experience shipping LLM-powered features or automations (e.g., prompting, RAG, tool/function calling, agentic orchestration, evals)
• ML/MLOps literacy (evaluation metrics, experimentation, feature stores, CI/CD, monitoring, drift)
• Familiarity with modern data stacks (e.g., Snowflake, dbt, AWS/SageMaker, Airflow/Dagster)
• Strong stakeholder leadership and communication skills, with the ability to work across technical and business teams
• Experience working in regulated environments (e.g., healthcare, life sciences, fintech)

Preferred

• Experience in healthcare, diagnostics, life sciences, and/or digital health
• Familiarity with healthcare data domains and systems, including patient, provider, payer, claims, billing, and clinical trial data
• Experience with vendor evaluation, platform selection, or external AI tooling ecosystems
• Advanced degree in a technical and/or business discipline

What Success Looks Like

• Production-grade data and AI products are deployed and embedded in day-to-day decision-making workflows
• Process automations and internal tools you build are reducing manual effort and improving output quality across the teams you support
• Measurable improvements seen in key business metrics (e.g., case completion turnaround time, patient satisfaction, cost-to-serve)
• Reliable, high-quality data foundations connect patient, provider, clinical, operational, and billing/financial insights end-to-end
• AI systems are continuously monitored, evaluated, and improved with measurable business impact
• Stakeholders trust and adopt implemented solutions across both corporate and business-unit teams
• Internal teams have transparent access to the function's roadmap, trust in delivery against it, and visibility into the evaluation logic that prioritizes initiatives based on their impact to organizational goals

About the role

The Forward Deployed Solutions Engineer will work directly within a business domain (e.g., Commercial, Clinical Operations, Lab Operations, Sales & Marketing, Customer Experience etc.). In your role, you’ll find opportunities for enhancing efficiency and productivity by looking for workflows which can be executed 10–100x faster or more often than a human team could using AI agents, integrations and other patterns. You will build, deploy, and run them in production.

You report into the central AI & Automation team, partner directly with domain leadership on priorities, and bring patterns back so the whole company compounds.

Find the leverage in your domain

• Map the workflows in your domain — the ones running today, and the ones that don’t exist yet because they weren’t feasible without agents or automation tools.
• Identify the step-change opportunities: where AI, ML, or automation unlock throughput, coverage, or speed.
• Build the business case, quantify projected impact, and align with domain leadership on priorities. 

Design the future-state workflow

• Map structured and unstructured data flows across the systems involved (CRM, ERP, ticketing, document stores, internal tools, external SaaS).
• Define the target workflow: what the agent does, what the human does, and where they hand off.
• Figure out what context the agent or model needs to do the work well — and how to get it there reliably (retrieval, grounding, tool access, memory).
• Design human-in-the-loop checkpoints so review adds value without becoming the bottleneck.

Build and connect the systems

• Stand up agents and automation pipelines using the organization’s approved AI platforms and frameworks.
• Connect agents to business systems — via MCP servers, APIs, webhooks, CLIs, and skills — within the guardrails set by IT and security.
• Configure tools, prompts, context, and retrieval pipelines so agents perform reliably on real work, not just in demos.
• Handle integration gnarliness: auth, schema drift, rate limits, data quality, and the messy last-mile of enterprise systems.
• Enable access and training for business to run the workflows

Run agents and automation pipelines in production

• Own agent performance end-to-end. Track the KPIs that matter — throughput, quality, cost, human intervention rate, cycle time, adoption.
• Build and manage evals. Re-run them on any material model, data, or workflow change before it ships.
• Triage failures, tune prompts and context, iterate on the workflow, and retire agents when they’re no longer the right tool.
• Instrument observability: tracing, structured logs, dashboards. You don’t ship what you can’t see.

What we’re looking for

• Hands-on technical fluency. CLIs, APIs, webhooks, SQL, and Python scripting. Working knowledge of LLM and agent behavior — prompting, context, tool use, RAG, MCP, evals, failure modes. Be very comfortable with a cloud platform.
• Trustworthy with elevated access. Least-privilege, auditability, and safe rollbacks are second nature.
• Strong technical and process judgment. You think in outcomes and KPIs, can defend prioritization calls, and are comfortable being the most technical person in a business meeting and the most business-savvy in a technical one.

Nice to have

• Prior experience working hand in hand with businesses to deliver measurable outcomes.
• Hands-on experience with an enterprise agentic platform (CrewAI, LangChain, AWS Bedrock, Claude, Codex) or building directly against a model API.
• Background in product management, solutions engineering, consulting, forward-deployed engineering, or technical operations.
• Experience in regulated environments (HIPAA, SOC 2, GxP, SOX).

Success in year one

• Shipped three or more workflows into production that are measurably moving a business KPI, with agent evals and observability in place.
• Domain leadership brings you into planning early, not late.
• Contributed at least one reusable asset another engineer on the team is now using.
• Enabling non builders to become builders using the artifacts you created.

Role Description

This is a high-autonomy, high-agency position for a builder who thrives in ambiguity and wants to ship AI products that directly impact cancer care. You'll sit at the intersection of engineering, product, and design, working across all three modalities to ship products to customers. Your work will span two critical domains:

1. AI Products for Providers 

Design and ship AI-powered experiences for oncologists and clinical teams. This includes exploring conversational AI experiences, building agent-based workflows that help providers navigate managing patient care, and bringing new clinical AI products to market. You'll spend meaningful time with customers to deeply understand their workflows and uncover where AI can create transformative value.

2. Commercializing Natera's AI Foundation Models

You'll be the bridge between Natera's AI research lab and the launched product. Our team has developed proprietary models that need to be brought to market and into the hands of clinicians. You'll contribute to training and post-training of these models, build the serving infrastructure, shape the user experience, and own the end-to-end deployments of these products.

What You'll Do

• Ship zero-to-one AI products end-to-end — from customer discovery and prototyping through production deployment and iteration
• Build agentic AI systems — design and implement autonomous and semi-autonomous workflows using LLMs, tool-use, memory, and orchestration
• Develop AI tools that improve efficiency across clinical operations, data extraction, manual workflows, and more
• Commercialize AI research — partner with the AI lab to take proprietary models from research prototypes and into production
• Contribute to model training and post-training — fine-tuning, evaluation, safety testing, and optimization of Natera's proprietary oncology models

What We're Looking For

Required

• 5+ years of software engineering experience, with at least 2 years focused on AI/ML or LLM-powered products in production
• Demonstrated ability to ship end-to-end — you've taken AI features or products from idea to production, ideally in a zero-to-one context
• Strong full-stack engineering skills — proficiency in Python and modern web frameworks; you can build both the model pipeline and the user-facing product
• Production experience with LLMs — fine-tuning, RAG, prompt engineering, agentic architectures, structured extraction, evaluation, and observability
• High agency and autonomy — you don't wait for permission, detailed specs, or hand-holding. You unblock yourself, seek out the highest-impact work, and drive it to completion
• Excellent communication — you can translate complex AI concepts for clinical and business stakeholders, and articulate a vision for what you're building and why

Preferred

• Experience in healthcare, biotech, diagnostics, or pharma — especially oncology
• Familiarity with clinical workflows, electronic health records, or provider-facing software
• Track record of working in regulated environments (HIPAA, FDA, CLIA, CAP)
• Background in or comfort with computational biology, genomics, or multimodal data (imaging, sequencing, clinical records)
• Experience with AWS infrastructure, Kubernetes, MLflow, or similar ML platform tooling

Prior experience in a high-growth startup or zero-to-one product environment

Job Summary:

Natera is seeking an experienced Senior Software Engineer with modern data engineering and AI-enabled development skills with deep scientific R&D background to design and build data products that directly support genomics research and translational science. This role is intended for someone who already understands how research organizations operate, how genomic data flows from experiment to insight, and how to engineer data systems that accelerate discovery without compromising rigor or compliance.

The ideal candidate combines strong data engineering skills with a computer science background and hands-on experience in bioinformatics, genomics, or computational biology, and the ability to work independently in an R&D environment. You will also be comfortable moving quickly to prototype novel data products while ensuring solutions evolve into robust, compliant, and scalable platforms. You will bring an internalized sense of what “good” looks like for research data: reproducibility, traceability, performance, and scientific usability.

Key Responsibilities

• Design, build, and maintain the data products that support R&D, analytics, Lab and scientific workflows, from initial design through deployment and iterations 

• Build and maintain data pipelines for large and complex datasets, from raw inputs through derived and analysis-ready datasets. 

• Apply domain knowledge in genetics and bioinformatics to design data models, schemas, and abstractions that align with real research patterns and downstream analysis needs.

• Design and enforce de-identification and privacy-preserving architectures that meet HIPAA and related regulatory requirements while remaining usable for research.

• Design scalable data models to power analytics, reporting, and downstream applications. Maintain high standards of data quality, accuracy, lineage, and observability across data pipelines.

• Partner closely with R&D scientists, bioinformatics teams, and software engineers to translate research needs into well-structured, reusable data assets.

• Optimize storage, retrieval, and lifecycle management for large scientific files (E.g. sequencing data, intermediate artifacts, derived datasets).

• Drive rapid prototyping efforts to support exploratory, proof-of-concepts, and early-stage initiatives, while guiding the transition to production-grade systems.

• Implement best practices for data quality, validation, lineage, observability, and reproducibility to enable a trusted 360° view.

• Collaborate with product managers and domain experts to translate requirements into technical solutions

• Establish golden paths (templates, examples, docs) and contribute to shared data product catalogs, patterns, and best practices used by other engineers

• Provide technical guidance and mentorship to mid-level engineers

Required Qualifications

• Bachelor’s or Master’s degree in computer science or bioinformatics with healthcare or biotech data domain experience preferred

• 8+ years of experience in data engineering, designing and maintaining data pipelines and cloud data architectures (e.g, Snowflake, AWS, etc)

• Strong background in bioinformatics, genomics, or computational biology (required).  Understands key genomics and bioinformatics data formats, such as BAM, VCF, FASTQ, common compression techniques for these file formats, and their storage, delivery, and management needs.

• Demonstrated experience supporting scientific R&D, Lab workflows and research teams with production-grade data systems. 

• Strong proficiency in Python, SQL, and distributed processing frameworks (Spark or equivalent)

• Experience with modern orchestration tools (Airflow, dbt, Dagster)

• Experience leveraging AI-assisted development tools (e.g., LLM copilots) to accelerate data solution development

• Familiarity with building data products that support analytics, ML, or AI applications

• Strong data modeling expertise (dimensional, normalized, healthcare-specific schemas)

• Experience implementing CI/CD for data pipelines and IaC (Terraform, CloudFormation); Knowledge of data observability, testing, and data quality frameworks

• Demonstrated ownership of production-grade data systems and end-to-end pipeline lifecycle

• Ability to evaluate emerging data and AI technologies and recommend scalable solutions

• Exposure to vector databases, embeddings, semantic search, or RAG-based architectures is a plus

• Proven ability to operate effectively in fast-paced environments, balancing speed, rigor, and compliance

• Strong written and verbal communication skills with ability to collaborate across engineering, analytics, and business stakeholders

• Experience working with healthcare, life sciences, or other highly regulated data, including hands-on HIPAA compliance.
  
  #LI-DNI

Role Overview

The Senior Software Engineer, Agentic RPA Automation is responsible for designing, building, and managing enterprise automation solutions through a combination of RPA, AI Agents, API engineering, and enterprise integration platforms. This role is for a senior hands-on software engineer who can take business needs and turn them into production-grade automation solutions that are reliable, maintainable, and secure. This role requires experience in both RPA and AI engineering to be successful.

In this role, you will partner with business stakeholders to drive delivery across high-value automation use cases. You will also help establish patterns, mentor engineers, and raise the quality bar for the automation function.

Key Responsibilities

Automation Solution Design and Delivery

• Design, build, test, deploy, and enhance enterprise automation solutions using RPA, AI Engineering, API Engineering, and system integration patterns

• Translate business requirements into end-to-end technical solutions that automate workflows across multiple systems and teams.

• Build automation workflows with strong error handling, exception management, retry logic, and observability.

• Deliver solutions that are scalable, supportable, and aligned with operational realities.

Agentic AI Automation and Workflow Orchestration

• Implement automation solutions that combine deterministic workflow execution with AI agent-based decisioning where appropriate.

• Select the right mix of RPA, APIs, enterprise integration platforms, and AI agent automation based on complexity, risk, and business value.

• Contribute reusable patterns for orchestration, tool use, and automation safety.

API Engineering and System Integrations

• Build and maintain integrations on AWS or enterprise integration platforms and API-led automation patterns as a core part of the automation stack.

• Design integrations that connect enterprise applications, data sources, and automation workflows in a secure and maintainable way.

• Partner with application and platform teams to define interface contracts, integration patterns, and dependencies.

• Ensure automation solutions are reusable, well-documented, and production-ready
  

Operational Excellence

• Support production automation solutions through issue resolution, root-cause analysis, and continuous improvement.

• Improve deployment quality, platform health, observability, and runtime stability.

• Establish strong engineering practices for testing, documentation, review, and controlled change management.
  

Technical Leadership

• Mentor software engineers and help establish strong technical standards for the automation team.

• Contribute to architecture reviews, design reviews, and implementation reviews for complex automation initiatives.

• Help define reusable frameworks and patterns that accelerate delivery across the portfolio.
  

Qualifications

Required

• 8+ years of experience in software engineering, automation engineering, integration engineering or a related field.

• 5+ years of experience in RPA platforms such as UiPath, Automation Anywhere, Blue Prism, Mulesoft RPA

• 2+ years of experience developing GenAI engineering solutions on AWS Bedrock, Azure, Google Vertex etc.

• Strong hands-on experience designing and building API integrations in AWS or on one or more enterprise integration platforms in a complex enterprise environment.

• Experience building and maintaining production-grade automation workflows with strong attention to reliability, supportability, and quality.

• Strong understanding of enterprise APIs, systems integration, and workflow orchestration.

• Experience translating business requirements into technical designs and production solutions.

• Strong debugging, problem-solving, and cross-functional collaboration skills.

• Current, role-relevant certifications in core automation platforms are required. If a required certification is not active at the time of hire, the candidate must be able to obtain or renew it within 30 days of start date. Employees in this role are expected to keep required certifications active thereafter.
  

Preferred

• Experience with intelligent automation platforms, AI-driven workflow solutions, and enterprise orchestration tools (e.g., leading RPA, iPaaS, and low-code automation platforms).

• Experience in healthcare, pharma, diagnostics, or other regulated environments.

• Experience mentoring engineers and influencing engineering standards through collaboration and continuous learning.

Role Overview

As the Director of Data & AI Governance, you will establish and lead enterprise-wide data management programs that ensure safe, compliant, high-quality data and AI. You will oversee Data Governance, AI Governance,  and Data + AI Stewardship, serving as the central authority on policies, forums, and controls across R&D, Lab Operations, Commercial, and SG&A domains. You will lead the Data & AI Governance Council through advocacy and a well thought data management strategy.

This role goes beyond policies into technical governance — it requires experience of how to build frameworks, deploy controls in code, and integrate governance into engineering delivery. The role spans all dimensions of governance, including: quality, privacy, security, agentic automation, AI risk management, bias/fairness testing, evals, and vendor AI evaluation. 

Key Responsibilities

Governance Council & Operating Model

• Define enterprise data management strategy and operating model and ensure that it is executed consistently across the enterprise.

• Chair and operationalize the AI & Data Governance Council, driving decision-making and accountability across legal, regulatory, compliance, IT, security, engineering, and product.

• Lead a federated stewardship model, ensuring business units own data while governance enforces consistency and compliance.

• Establish governance forums (steering committees, working groups, architecture boards) with clear outcomes.

Data Governance & Quality

• Build and drive adoption of 360° master/reference datasets (e.g., Case360, Patient 360, Provider 360, Billing 360) and ensure they are maintained as sources of truth for analytics and AI

• Partner with engineering teams to build interoperable standards that can be used to connect domain datasets to create longitudinal data products

• Define and enforce enterprise data governance policies, ensuring consistency in data definitions, lineage, and stewardship across all domains

• Build and manage enterprise data catalogs and metadata services to make data discoverable, trustworthy, and reusable across the organization.

• Establish and operate data quality frameworks with validation rules, anomaly detection, and automated testing to ensure accuracy, completeness, and timeliness.

• Embed data quality checks and lineage tracking directly into data and AI pipelines so that governance guardrails can be adopted without friction.

AI Policy Engineering & Implementation

• Develop AI use case risk management framework (RMF) to evaluate AI use cases from a governance, regulatory, medical, privacy, security, and risk standpoint

• Build and maintain an AI risk register and incident response plan for all AI use cases

• Develop governance policies (privacy, security, quality, fairness, integrity) aligned to HIPAA, CLIA, FDA, GDPR, and emerging AI regulations.

• Translate policies into technical implementations by embedding controls into:

  • ETL pipelines, feature stores, and model registries

  • CI/CD workflows for ML/GenAI models

  • Prompt orchestration and output logging for LLMs

  • Bias/fairness testing, drift detection, explainability dashboards

AI Risk & Automation

• Build and execute agentic automation processes and associated guardrails to enable business process automation

• Build documentation and process to ensure agent accountability through change history, audit logs, versioning etc.

• Track external regulatory trends and industry standards (e.g., NIST AI RMF, EU AI Act, FDA AI/ML guidance) 

AI Change Management & Vendor Governance

• Lead AI change management initiatives, including training programs, awareness campaigns, and a network of governance champions to drive adoption of best practices.

• Partner with Corporate Communications to cascade governance updates, AI guardrails, and usage guidelines across all levels of the organization.

• Develop and enforce vendor and third-party AI evaluation frameworks, assessing external AI tools for governance, data security, model risk, and compliance posture before integration.

• Track and manage vendor AI risks through standardized assessments, approvals, and monitoring processes.

Qualifications

Required:

• 15+ years in Data or AI governance with at least 7+ in leadership roles.

• Excellent stakeholder management and executive communication skills.

• Proven track record of building and leading enterprise-wide governance programs, councils, and stewardship networks that span both data governance (catalogs, lineage, quality) and AI/ML governance (model risk, bias/fairness, explainability, monitoring).

• Deep understanding of healthcare regulatory frameworks impacting AI: HIPAA, CLIA, FDA, GDPR, and regulations (EU AI Act, NIST AI RMF, Japan AI Promotion Act).

• Experience with LLM/GenAI governance implementation as per common regulatory frameworks: prompt logging, bias testing, guardrails, explainability.

• Experience in vendor AI/ML evaluation and governance, including due diligence of third-party AI/ML tools and platforms.

Preferred:

• Certifications in data governance, privacy, or risk management (DAMA CDMP, CIPP, CRISC)

• Advanced degree (MS/PhD) in Computer Science, AI/ML, engineering or related field.

• Experience driving AI change management and building a culture of responsible AI adoption (training, awareness, champion networks).

• Proven experience embedding governance in code (pipelines, registries, CI/CD).