Staff Data Science Engineer, Siri Runtime Systems and Interaction

As part of Siri Attention and Invocation team, we collaborate to deliver the next revolution in human-computer interaction, to inspire and create groundbreaking technology for large scale systems, spoken language, big data, and artificial intelligence, to overcome real-world challenges through innovation and user-centered systems that focus on improving the daily life of millions of our customers. We are seeking an exceptional Staff Data Science Engineer to join our team and drive strategic data science initiatives across the organization. In this role, you will lead the development of advanced machine learning solutions, mentor junior team members, and shape our data science roadmap. Key Responsibilities: Lead complex data science projects from conception through production development Establish best practices for model validation, and monitoring Drive technical decisions and architecture for ML/AI initiatives Identify high-impact opportunities where data science can drive business value Collaborate with product, engineering, and business stakeholders to define requirements Stay current with emerging technologies and methodologies in data science and ML Contribute to the company's technical vision and data strategy Education: MS or PhD in Computer Science, Statistics, Mathematics, or related field (or equivalent experience) Experience: 5+ years in data science, machine learning, or related fields Technical Skills: Expert proficiency in Python and/or R Deep understanding of machine learning algorithms and statistical methods Strong SQL skills and experience with big data technologies (Spark, Hadoop, etc.) Leadership: Proven track record of leading technical projects and mentoring teams Communication: Excellent ability to explain complex technical concepts to non-technical stakeholders Proficiency with cloud platforms (AWS, GCP, or Azure) Experience with ML frameworks (TensorFlow, PyTorch, scikit-learn) Familiar with workflow orchestrators like Airflow or Kubeflow Experience deploying models to production environments

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