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