Sr. Product Manager - AI & Data (Sales, Marketing, & GM Focus)

We are seeking a Senior 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 specifically on building and scaling platforms and products that power decision intelligence across these domains, such as sales forecasting, commercial engagement performance, and clinical/operational insights.

You will operate in an embedded model, deeply aligned 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.

This is a technical product role requiring fluency in data systems, modern data platforms, ML, and emerging AI patterns (e.g., LLMs, agentic systems), 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 that influence operational decision-making at scale and overall organizational success.

• 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

• 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 health assessments

• 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)

• Define and implement AI product patterns, including agentic workflows and RAG

• Establish evaluation frameworks for LLM-powered features (faithfulness, relevance, safety, cost, latency)

• Partner with engineering to implement prompt strategies, guardrails, and continuous evaluation pipelines

• Drive build vs. buy decisioning and proofs of concept

• Ensure data 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

• 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 without direct authority

• 7–10+ years of product management experience, 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 acumen (e.g., querying data, working in notebooks for EDA and experimentation, modeling datasets)

• ML/MLOps literacy (evaluation metrics, experimentation, feature stores, CI/CD, monitoring, drift)

• Experience shipping LLM-powered or AI products (prompting, RAG, evaluation frameworks, guardrails)

• 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)

• Advanced degree in a technical and/or business discipline

• 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

• Production-grade data and AI products are deployed and embedded in day-to-day decision-making workflows

• Measurable improvements 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

• Strong stakeholder trust and adoption 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

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