Director, Applied AI and Machine Learning

• Set the AI and machine learning strategy for Duck Creek Intelligence, connecting model and platform investments to product priorities, customer needs, and business outcomes.
• Lead the model lifecycle from problem framing and data readiness through model selection, training, refinement, fine-tuning, evaluation, deployment, monitoring, and ongoing improvement.
• Guide teams in choosing the right approach for each use case, including foundation models, task-specific models, traditional machine learning, and hybrid methods. Establish when to use, adapt, replace, or retire a model.
• Build a shared foundation for model access and routing, including clear selection criteria, orchestration patterns, fallback behavior, versioning, and operational controls across internal and external models.
• Establish an AI evaluation practice that combines offline benchmarks, task-specific quality measures, expert review, safety and reliability checks, and production feedback. Make evaluation results useful to Product and Engineering decisions.
• Build and operate MLOps and model platform capabilities for reproducible training, deployment, observability, performance and cost management, and safe iteration in production.
• Lead, coach, and grow data scientists and ML operations and engineering teams. Set clear goals, develop technical leaders, and create effective collaboration across disciplines.
• Partner closely with Product, Engineering, architecture, security, legal, and insurance subject matter experts to turn real workflows and domain knowledge into well-scoped, measurable AI solutions.
• Represent Duck Creek in strategic model and technology partnerships. Evaluate providers and lead technical and commercial discussions, including partnership and model-development negotiations with major vendors such as Google, in coordination with internal stakeholders.
• Set practical standards for data quality, model documentation, privacy, security, responsible AI, governance, and human oversight throughout the model lifecycle.
• Communicate complex technical trade-offs clearly to executives and cross-functional teams, creating alignment on priorities, risks, investment decisions, and success measures.

• 10+ years of experience in machine learning, data science, AI engineering, or a related technical discipline, including meaningful hands-on work in developing and improving models.
• 5+ years leading technical teams, including data scientists and teams responsible for ML platforms, model operations, or production ML systems.
• Direct experience with model training and refinement, and practical experience selecting models and applying fine-tuning or other adaptation techniques to meet real product requirements.
• Delivered machine learning or AI capabilities into production, with accountability for model quality, reliability, monitoring, iteration, and business impact.
• Designed or led model evaluation approaches that measure quality against representative tasks and inform model, prompt, routing, or product decisions.
• Experience partnering with Product and Engineering teams to define use cases, build technical foundations, and ship capabilities used by customers or internal teams.
• Experience working with external AI, cloud, or data vendors, including evaluating technical fit and leading partnership or commercial discussions with procurement, legal, and executive partners.
• Strong communication and stakeholder skills, with the ability to explain technical trade-offs and build alignment across executive, engineering, product, commercial, and domain-expert audiences.
• Bachelor's degree in computer science, data science, engineering, mathematics, or a related field, or equivalent practical experience.

• Graduate degree in a relevant technical field.
• Experience with large language models, retrieval-augmented generation, agentic workflows, or other generative AI patterns, alongside a sound understanding of when simpler methods are more appropriate.
• Experience building model gateways, routing and orchestration systems, evaluation platforms, feature or data pipelines, and cloud-native ML infrastructure.
• Experience in SaaS, enterprise software, insurance, or another domain with complex workflows, sensitive data, and high standards for reliability.
• Knowledge of responsible AI practices, security and privacy controls, model risk management, and human-in-the-loop review.

• Teams have a clear, repeatable way to select and evaluate models based on product needs, quality, reliability, risk, and cost.
• Model training, adaptation, deployment, routing, and monitoring are supported by reusable capabilities that help teams move from experimentation to dependable production use.
• Data scientists, ML operations, Product, Engineering, and insurance experts work from shared priorities and measurable outcomes.
• External partnerships accelerate model and platform development while serving Duck Creek's technical, commercial, and customer needs.

Travel: Up to 25% Work Authorization: Legally authorized to work in the country of job location. The Company does not sponsor visa petitions for this position.

Actual compensation for this role is determined based on a variety of factors, including job-related skills, experience, qualifications, internal equity, and geographic location, where permitted by law.

For reference, the expected salary range for this position is generally positioned as follows based on candidate location:

• 233,700 - 333,900: Candidates living in the Boston, New York City-Newark, Los Angeles, San Francisco/Bay Area, Seattle, or Washington, DC metropolitan areas
• 222,700 - 318,200: Candidates living in Chicago, Dallas-Fort Worth, Houston, Minneapolis, or Philadelphia metropolitan areas, or in Colorado, Connecticut, Maryland, Rhode Island, or Vermont
• 211,700 - 302,400: Candidates living in Georgia, Michigan, North Carolina, New Hampshire, Oregon, or Wisconsin
• 187,500 - 267,800: For all other U.S. locations

This position is also eligible for annual bonus compensation, subject to plan terms and individual eligibility.

Final compensation will be determined based on the factors outlined above and in accordance with applicable law.

Duck Creek Technologies believes in the safe, ethical, and responsible use of artificial intelligence. We may use AI-assisted tools to support parts of the recruitment process, such as reviewing applications, identifying relevant skills, and organizing candidate information.

AI tools are used to assist - not replace - human judgment. All AI-assisted outputs are reviewed by a Duck Creek team member, and final hiring decisions are always made by people. Candidates should be comfortable working in environments where AI is used responsibly to enhance business processes.

Duck Creek Technologies offers a comprehensive benefits package, which includes:

• Flexible work environment
• Medical, dental, vision, life, and disability insurance
• 401(k) retirement plan with match (6% employer match up to $12,000 annually)
• Flexible Spending Accounts (FSA) and Health Savings Accounts (HSA)
• Paid holidays, vacation, and volunteer time
• Employee Assistance Program (EAP)

Benefits eligibility and offerings may vary based on role and location.

The application window for this position will remain open until the role is filled or a sufficient number of qualified candidates have been identified.