Middle Applied Data Scientist - Operations Decision Intelligence - Jobs - Careers at Apple
- 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 that serve as the single source of truth across human-in-the-loop workflows and vendor execution.
- Develop practical, scalable decision-support tooling and simulation capabilities that allow operations leads to evaluate trade-offs, model capacity constraints, and optimize spend in real time.
- Build and evolve intelligent, conversational automation and tool-calling workflows that streamline operational inquiries and turn manual triage into self-service actions.
- Establish monitoring and anomaly detection systems across operational metrics to proactively identify pipeline bottlenecks, SLA risks, and spend variances before they impact delivery.
- Partner closely with downstream users of these systems—operations leads, evaluation engineers, and program managers—to ground tooling design in real feedback, daily workflows, and evolving feature needs.
- Communicate analytical findings, capacity forecasts, and tooling roadmaps clearly to both technical and operational stakeholders.
- 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.
- 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.
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Required Skills
Required Languages
🇬🇧 English