Lead Data Scientist - Alteryx

Meet the Moment with Alteryx

We're living through a once-in-a-generation shift in how work gets done. Data, automation, and AI are quickly becoming the center of every business decision - and Alteryx is leading the transformation.

You'll be working on the challenges that sit at the heart of modern business. No matter your role, the work you do will help organizations move faster, see more clearly, and tackle questions that used to feel impossible.

If you're ready to meet the moment with innovation, curiosity, and excellence, there's a place for you here.

Alteryx is searching for a Lead Data Scientist . This position is remote-friendly.

Position Overview:

We are seeking a Lead Data Scientist to join the AI Integrity Labs team within the AI organization. This role focuses on evaluating LLMs and agentic systems, developing robust testing methodologies, and driving telemetry analysis and associated frameworks. The ideal candidate will provide technical leadership in shaping next-generation AI-powered solutions that support our products. This includes designing evaluation frameworks, defining quality and performance metrics, and leveraging a strong understanding of agent architectures (e.g., LangChain, LangGraph) to support and enhance engineering efforts.

Primary Responsibilities:

Responsible for designing evaluation frameworks, testing methodologies, and telemetry analysis for agentic systems, with working knowledge of LangChain and LangGraph to support and enhance engineering efforts.

Build, validate, and optimize data models focused on generative AI and large language models.

Create and refine methods to measure AI model and agent performance, quality, and impact.

Collaborate with product and engineering teams to inform and support integration of AI solutions (e.g., Gemini, OpenAI, Azure AI).

Stay current on advances in data science, machine learning, and generative AI.

Mentor junior data scientists and engineers.

Communicate clearly with stakeholders, presenting insights and recommendations.

Apply statistical, econometric, and machine learning methods to generate actionable insights and support decision-making.

Identify, integrate, and leverage key data sources to enhance analytics.

Develop scalable data structures and analytic products.

Lead end-to-end analytics and modeling projects, including data gathering, feature engineering, model development, evaluation, and monitoring.

Provide insights to encourage data-driven decision-making throughout the organization.

Mentor teams on data science best practices and contribute thought leadership through blogs or articles.

Develop and deliver training on analytics tools and data science techniques (e.g., Alteryx , Tableau).

Explore and apply new data science tools and methodologies to maintain innovation.

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