Technical Product Manager- AI/ML

What You'll Do

Avalara is an AI-first company. We expect every employee to actively leverage AI to enhance productivity, quality, innovation, and customer value. AI is embedded in our workflows and products and success at Avalara requires embracing AI as an essential capability, not an optional tool.

As an AI/ML Technical Product Owner at Avalara, you will lead the development and integration of cutting-edge AI and Machine Learning solutions into our suite of tax compliance products. Youll collaborate closely with cross-functional teams, including Product managers, ML Engineers, Software Development Engineers, Content Researchers and designers, to conceptualize, build, and deploy models that drive significant business impact. Your role will involve translating complex technical concepts into strategic product initiatives, ensuring the successful execution of these initiatives from brainstorming to deployment. You will report to Director of Engineering.

What Your Responsibilities Will Be

Product strategy and vision

Define and drive the vision, strategy, and roadmap for AI & ML products, aligning them with Avalaras overall business goals and customer needs.
Identify new AI & ML opportunities by staying updated on industry trends, competitive landscape, and emerging technologies.
Communicate product vision and strategy to stakeholders, including executives, engineering teams, and customers.Cross-functional leadership

Collaborate with data scientists, engineers, and designers to define, prioritize, and execute AI/ML product initiatives.
Work closely with engineering teams to translate product requirements into technical specifications and ensure delivery of high-quality features.Product development and execution

Oversee the end-to-end product development lifecycle, from ideation and prototyping to testing, deployment, and iteration.
Manage the backlog, prioritizing features and improvements based on customer feedback, market research, and business impact.
Ensure the AI & ML models are properly integrated into products, focusing on scalability, performance, continuous improvementData-driven decision making

Use data to inform product decisions, leveraging metrics, user feedback, and A/B testing to guide development priorities.
Monitor the performance of AI & ML models and products post-launch, using analytics to identify areas for improvement and optimization.Stakeholder communication and reporting

Present product updates, milestones, and performance metrics to executive leadership and other stakeholders.
Facilitate regular meetings with cross-functional teams to ensure alignment and address any challenges or roadblocks.
Prepare detailed reports on the progress of AI & ML initiatives, including successes, challenges, and next steps.Ethical AI considerations

Ensure that AI & ML products adhere to ethical guidelines and industry best practices, including fairness, transparency, and data privacy.
Advocate for responsible AI practices within the organization, ensuring that models and data usage align with legal and ethical standards.Customer and market insights

Engage with customers, users, and industry experts to gather insights on market needs and pain points.
Use these insights to inform product features and improvements, ensuring that the AI & ML products deliver real value to end users.

What You'll Need to be Successful

Educational Background: Bachelor's degree in Artificial Intelligence, Machine Learning, Data Science, or Computer Science. Advanced degrees or certifications in AI & ML
Product Management Experience: Experience of managing AI/ML products from conception to launch for at least 8 years, including experience with roadmaps, prioritization, and stakeholder management. Envisage new products from the perspective of the end user in B2B and B2C contexts.
Technical Skills: Proficiency in machine learning frameworks (e.g., PyTorch, TensorFlow), programming languages (e.g., Python, R, C++), and cloud platforms (e.g., AWS, GCP, Azure). Familiarity with data engineering pipelines, model deployment, and MLOps.