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

Role Overview

 

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.

 

 

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

 

Discovery, Experimentation, & Requirements

• 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

 

Delivery, Data, & ML Lifecycle

• Partner with data and AI/ML engineering resources to deliver scalable products and capabilities

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