Sr. Analytics Engineer AI-ML

About the Role

Key Responsibilities

• Design and implement machine learning models for anomaly detection, predictive analytics, and root cause analysis to support faster insights for Workday's systems observability
• Develop correlation engines using complex ML algorithms to identify patterns and relationships in sophisticated multi-dimensional datasets
• Create and optimize feature engineering pipelines that transform raw observability data into actionable ML features
• Implement and deploy ML models for service outage prediction and performance optimization
• Connect data to actionable model output to help reduce MTTD/MTTR
• Build intelligent agentic systems that can autonomously monitor, diagnose, and recommend remediation actions for system health and performance issues
• Design and implement LLM-powered solutions for automated log analysis, incident analysis, and natural language querying of observability data
• Collaborate with data engineers to design ML-optimized data schemas and storage solutions
• Develop real-time inference systems capable of processing high-velocity streaming data with low latency
• Research and prototype next-generation AI techniques including deep learning, time series forecasting, and unsupervised learning methods
• Work closely with analytics leaders and product teams to translate business requirements into technical ML solutions
• Contribute to the development of internal ML platforms and tools that enable self-service AI capabilities

Requirements

• 3+ years of hands-on experience building and deploying machine learning models in production environments
• 5+ years of software engineering experience with focus on data-intensive applications
• Bachelor's degree in Computer Science, Machine Learning, Data Science, or equivalent work experience
• Strong programming skills in Python and experience with ML frameworks (TensorFlow, PyTorch, Scikit-learn)
• Experience with big data processing frameworks (Spark, Kafka) and distributed computing
• Proven experience with MLOps tools and practices (MLflow, Kubeflow, Docker, Kubernetes)
• Understanding of statistical analysis, time series analysis, and anomaly detection techniques
• Experience with real-time ML inference systems and streaming data processing
• Familiarity with cloud ML services (AWS SageMaker, GCP AI Platform, Azure ML)
• Experience with feature stores, model registries, and ML experiment tracking
• Knowledge of data visualization and monitoring tools (Grafana, Prometheus, Superset)
• Strong understanding of software engineering methodologies including version control, testing, and CI/CD
• Ability to identify opportunities where AI/ML can improve operational efficiency and incident triaging
• Experience with SQL and NoSQL databases for ML feature storage and retrieval
• Excellent interpersonal skills for collaboration and explaining technical concepts to non-technical stakeholders
• Self-motivated with the ability to work independently and drive projects to completion

Nice to Have

• Experience with cloud ML services (AWS SageMaker, GCP AI Platform, Azure ML)
• Knowledge of data visualization and monitoring tools (Grafana, Prometheus, Superset)

Qualifications

• Bachelor's degree in relevant field or equivalent work experience

Benefits & Perks

• Competitive salary with base pay ranges from $186,600 to $279,900 depending on location
• Potential eligibility for Workday Bonus Plan, role-specific commission/bonus, and annual stock grants
• Flexible work approach combining in-person and remote work, with at least 50% in-office time each quarter
• Opportunities to work in a collaborative, employee-centric environment that values personal development and contribution