Principal Data Scientist

Position Summary

As a Principal Data Scientist, you will serve as the visionary and enterprise-wide AI leader, responsible for shaping the long-term AI strategy and ensuring the organization remains at the forefront of innovation. You will set the research agenda, influence executive decision-making, and represent the company’s AI capabilities externally to customers, partners, and the industry. While Senior Staff drives strategy within the org, Principal defines the horizon, aligning business direction with scientific breakthroughs and ensuring that AI investments yield transformative impact.

Key Responsibilities

AI Vision & Thought Leadership

• Define and champion the long-term AI vision of the company.
• Anticipate industry shifts (e.g., AGI, regulation, ethical AI, multi-modal systems) and prepare the org to adapt.
• Shape internal and external conversations on responsible, scalable, and impactful AI.

Strategic Business Alignment

• Partner with executive leadership (CTO, CPO, CEO) to connect AI strategy with business growth, market positioning, and customer needs.
• Identify the highest-leverage opportunities for AI to transform product lines, workflows, or new revenue streams.
• Make investment recommendations (build vs. buy vs. partner) for long-term advantage.

Scientific & Technical Leadership

• Push boundaries by sponsoring cutting-edge research (e.g., foundation models, domain-specific GenAI, neuro-symbolic AI).
• Drive IP creation through patents, publications, and open-source leadership.
• Define enterprise-wide AI standards, architectures, and ethical frameworks.
• Provide final guidance on complex architectural trade-offs with company-wide implications.

Cross-Org & Industry Influence

• Mentor Senior Staff and Staff Data Scientists, creating a pipeline of future leaders.
• Serve as the external face of AI at conferences, research consortiums, and client CxO discussions.
• Build strategic partnerships with academia, research labs, and industry consortia to bring new innovations in.

Governance & Risk Management

• Establish AI governance policies for fairness, compliance, security, and explainability.
• Influence external regulatory and standards bodies to shape the future of responsible AI.
• Ensure AI adoption scales safely, reliably, and with business continuity.

Qualifications

• PhD (preferred) or Master’s with 12–15+ years of experience in AI/ML research and applied product delivery.
• Recognized leader with industry publications, patents, or external visibility.
• Proven record of shaping enterprise-wide AI strategy and delivering lasting business impact.

Preferred Attributes

• Visionary mindset: sees beyond current problems to the future of the field.
• Influential communicator who can bridge executives, researchers, and engineers.
• Strong presence in the AI community (thought leadership, keynote talks, collaborations).
• Capable of balancing research innovation with business pragmatism.