Senior Manager, Applied AI and Analytics

About the Role

The team will create both applied machine‑learning solutions—leveraging off‑the‑shelf and custom models—and analytics infrastructure such as ETL pipelines, observability layers, dashboards, and KPI reporting. By integrating these capabilities into CI/CD and MLOps pipelines, the group will turn data and ML investments into measurable product and business outcomes. Collaboration is central to the role; the leader will partner closely with Product Management, UX, Platform, and Engineering teams to scope work, prioritize outcomes, and drive timely delivery. They will also serve as a technical and people leader, influencing product decisions, defining success metrics, and presenting findings to executive stakeholders. • Lead and grow a high‑performing Applied AI & Analytics team (6‑10 direct reports), handling hiring, mentorship, and career development. • Define and execute the technical strategy and roadmap for AI and analytics aligned with product and business goals. • Deliver production‑grade AI/ML features by integrating off‑the‑shelf and custom models into Autodesk’s product stack and CI/CD/MLOps pipelines. • Build and own analytics platforms including data ingestion/ETL, modeling, observability, reporting, and dashboards. • Implement robust MLOps practices, testing, monitoring, A/B experiments, and feature‑flag rollouts for models and data pipelines. • Partner with Product Management, UX, Platform, and Engineering to scope work, prioritize outcomes, and ensure timely delivery. • Translate business needs into scalable data and AI solutions, defining success metrics, KPIs, and SLAs. • Establish best practices for model governance, data quality, instrumentation, and reproducible analytics workflows. • Represent the team in stakeholder and executive reviews, presenting findings and recommendations persuasively.

Key Responsibilities

• ▸team leadership
• ▸ai strategy
• ▸model deployment
• ▸data platform
• ▸mlops
• ▸analytics dashboards

What You Bring

Autodesk is seeking an experienced engineering leader to head a combined Applied AI and Analytics organization. The role will guide a cross‑functional team of Applied AI and Analytics engineers, own roadmap execution, and ensure production‑grade AI features and world‑class analytics are delivered across Autodesk products. Success requires hands‑on knowledge of modern ML/AI stacks, data platforms, and a proven ability to hire, coach, and manage large programs in a matrixed environment. • 8+ years of experience in software engineering, data engineering, machine learning, or analytics; 3+ years of people‑management experience in a matrix environment. • Proven track record of building and shipping production ML/AI systems and operating data/analytics platforms at scale. • Strong technical fluency in Python, ML frameworks (PyTorch/TensorFlow), MLOps tools, AWS (preferred), Docker/Kubernetes, and SQL/ETL pipelines (dbt or similar). • Experience with analytics and visualization tools such as Looker or Tableau and building robust reporting/observability pipelines. • Excellent cross‑functional communication and stakeholder management skills; ability to translate technical trade‑offs into business outcomes. • Strong bias for operational rigor—testing, monitoring, data quality, and reproducibility. • Bachelor’s degree in Computer Science, Engineering, Statistics, or equivalent; advanced degree a plus. • Preferred: background in design/manufacturing/AEC, familiarity with C++/TypeScript, product experimentation (A/B testing), and experience structuring combined Applied AI + Analytics organizations.

Requirements

• ▸python
• ▸aws
• ▸docker
• ▸kubernetes
• ▸looker
• ▸bachelor's degree

Benefits

Autodesk’s culture emphasizes innovation, collaboration, and a sense of belonging. Employees are encouraged to work on projects that shape a better world—from sustainable buildings to cutting‑edge factories—while benefiting from a competitive compensation package that includes base salary, bonuses, stock grants, and comprehensive benefits.

Work Environment

Hybrid