Applied Data Scientist - Operations Decision Intelligence

Apple Services Engineering (ASE) powers the AI and LLM features behind experiences that hundreds of millions of users love every day. As these systems increasingly rely on human-in-the-loop evaluation, the quality of our products is directly constrained by the speed of our evaluation processes. We believe that to build exceptional AI, you need exceptional evaluation operation tools. Apple Services Engineering (ASE) powers the AI and LLM features behind experiences that hundreds of millions of users love every day. As these systems increasingly rely on human-in-the-loop evaluation, the quality of our models is directly constrained by how fast, accurately, and systematically our operations can scale. We believe that to build exceptional AI, you need exceptional operational intelligence. In this role, you will own development of the data systems, predictive models, and self-service automation tools that optimize workforce capacity, forecast volatile demand, and transform evaluation operations into an agile, AI-driven platform.

The ML Data Quality Operations team within Human-Centered AI is looking for an Applied Data Scientist to take technical ownership of our core Operations Intelligence and Decision Support stack. In this role, you will design, scale, and evolve the systems that drive systematic workforce planning, predictive demand forecasting, spend optimization, and real-time operational telemetry across our human-in-the-loop pipelines. You will bridge applied quantitative modeling, data engineering, and intelligent automation—developing algorithmic forecasting models that absorb demand volatility and building conversational, tool-driven workflows that enable self-service operational decision-making. Your work will eliminate operational friction, turn complex resource and capacity constraints into automated decisions, and directly expand the throughput of Apple’s features evaluation workflows. This role demands a pragmatic builder with fluency across data systems and applied modeling: you will prototype, validate, and ship practical, scalable tooling that empowers operations leads and engineering partners across Apple.

Minimum Qualifications

• 3–5+ years of industry experience in applied science, data engineering, or operations research, with demonstrated experience building practical data systems or predictive models.
• Strong programming proficiency in Python and SQL, with hands-on experience designing, deploying, and maintaining resilient data pipelines and backend utilities.
• Experience developing quantitative or predictive models (e.g., time-series forecasting, resource allocation, capacity planning, or constrained optimization) applied to operational or business problems.
• Experience building or integrating intelligent automation, conversational interfaces, or tool-calling/retrieval workflows using modern LLM APIs to automate complex processes.
• Demonstrated experience translating fragmented operational data streams into unified data models, automated monitoring systems, and actionable decision tools.
• Demonstrated ability to work directly with cross-functional users to incorporate feedback into tooling design, and to communicate technical concepts clearly to non-technical partners
• MS or PhD in Computer Science, Data Science, Operations Research, Statistics, or a related quantitative field, or equivalent practical experience.

Preferred Qualifications

• Experience supporting Data Operations, Human-in-the-Loop (HITL) annotation pipelines, or AI/ML features evaluation workflows.
• Experience designing internal tools, services, or interfaces (e.g., lightweight web frameworks like FastAPI, Streamlit, or similar) that are intuitive, configurable, and extensible by practitioners who did not build them.
• Familiarity with workflow orchestrators (e.g., Airflow or similar engines) and containerized deployment patterns.
• Demonstrated passion for leveraging AI, automation, and decision intelligence to eliminate manual operational drag and scale organizational efficiency.