Applied Data Scientist - Operations Decision Intelligence

Applied Data Scientist role in Apple Services Engineering (ASE) focused on owning an Operations Intelligence and Decision Support stack.

Responsibilities

• Design and implement predictive forecasting and capacity allocation models to project evaluation demand, workforce utilization, and budget trajectories under high volatility.
• Build, automate, and maintain resilient data pipelines and operational telemetry used as a single source of truth across human-in-the-loop workflows and vendor execution.
• Develop decision-support tooling and simulation capabilities that help operations leads evaluate trade-offs, model capacity constraints, and optimize spend in real time.
• Create and evolve intelligent conversational automation and tool-calling workflows to streamline operational inquiries and convert manual triage into self-service actions.
• Establish monitoring and anomaly detection across operational metrics to identify pipeline bottlenecks, SLA risks, and spend variances before they impact delivery.
• Work closely with downstream users, including operations leads, evaluation engineers, and program managers, to align tooling with real feedback and daily workflows.
• Communicate analytical findings, capacity forecasts, and tooling roadmaps to technical and operational stakeholders.

Requirements

• Experience supporting Data Operations, Human-in-the-Loop (HITL) annotation pipelines, or AI/ML features evaluation workflows.
• Experience designing internal tools, services, or interfaces using lightweight web frameworks (example frameworks include FastAPI, Streamlit, or similar) that are intuitive and extensible for practitioners.
• Familiarity with workflow orchestrators (example: Airflow or similar) and containerized deployment patterns.
• Demonstrated passion for applying AI, automation, and decision intelligence to reduce manual operational drag and improve organizational efficiency.
• 3-5+ years of industry experience in applied science, data engineering, or operations research, including building practical data systems or predictive models.
• Strong Python and SQL programming skills, with hands-on experience building and maintaining resilient data pipelines and backend utilities.
• Experience developing quantitative or predictive models for operational or business problems (examples include time-series forecasting, resource allocation, capacity planning, or constrained optimization).
• Experience building or integrating intelligent automation, conversational interfaces, or tool-calling/retrieval workflows using modern LLM APIs to automate complex processes.
• Demonstrated ability to translate fragmented operational data streams into unified data models, automated monitoring systems, and actionable decision tools.
• Ability to partner directly with cross-functional users to incorporate feedback into tooling design and explain 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.

Technology

• Python
• SQL
• FastAPI
• Streamlit
• Airflow
• LLM APIs

Benefits

• Comprehensive medical and dental coverage
• Retirement benefits
• Discounted products and free services
• Reimbursement for certain educational expenses, including tuition for formal education related to advancing career
• Opportunity to become an Apple shareholder through participation in Apple’s discretionary employee stock programs
• Eligible for discretionary restricted stock unit awards
• Ability to purchase Apple stock at a discount if voluntarily participating in Apple’s Employee Stock Purchase Plan
• May be eligible for discretionary bonuses or commission payments, as well as relocation

Role Description

The ML Data Quality Operations team within Human-Centered AI is looking for an Applied Data Scientist to take technical ownership of core Operations Intelligence and Decision Support systems.

• Design, scale, and evolve systems for workforce planning, predictive demand forecasting, spend optimization, and real-time operational telemetry across human-in-the-loop pipelines.
• Bridge applied quantitative modeling and data engineering by building forecasting models that absorb demand volatility and conversational, tool-driven workflows for self-service operational decision-making.
• Reduce operational friction by automating decisions from complex resource and capacity constraints, improving throughput for Apple’s features evaluation workflows.
• Prototype, validate, and ship practical, scalable tooling that enables operations leads and engineering partners.

Location: Seattle, WA (onsite)

Compensation: USD 137,500 - 207,100 per year (base pay depends on skills, qualifications, experience, and location)

Note: Apple benefits, compensation, and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.