Applied Data Scientist, Operations Decision Intelligence

Apple Inc

Confirmed live today High trust

Quick summary

Work type
On-site
Location
Seattle, WA
Salary
$137,500–$207,100 / yr
Posted
18 days ago
Freshness
Confirmed live today

Market check

Salary context

Competitive pay

How this pay compares to similar roles

Similar $172k
This role $172k
$119k $231k
below market most similar roles pay here above market

This role pays more than 50% of similar roles. Most pay $129,677–$213,346 — the blue band above. At the midpoint, this role pays about $172k versus about $172k for comparable roles.

Based on 240 similar postings.

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About Apple Inc

Apple Inc. is a multinational technology company known for designing and manufacturing consumer electronics, software, and online services, including the iPhone, Mac, iPad, and App Store. Industry: Consumer Electronics & Software

Apple Inc currently has 3497 open roles on FindRole.

Listed pay typically runs $174,149–$277,600 across 2702 roles with salary data.

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At a glance

TL;DR · Applied Data Scientist, Operations Decision Intelligence

The Applied Data Scientist - Operations Decision Intelligence joins the ML Data Quality Operations team within Human-Centered AI. In this role, you will own the development of data systems, predictive models, and self-service automation tools to optimize workforce capacity and forecast volatile demand. You will design and implement predictive forecasting and capacity allocation models, build resilient data pipelines, and develop conversational automation workflows using modern LLM APIs. Key responsibilities include establishing monitoring systems for anomaly detection and creating decision-support tooling to optimize spend. You must demonstrate proficiency in Python, SQL, and quantitative modeling, specifically time-series forecasting and constrained optimization. This position solves the technical challenge of scaling human-in-the-loop evaluation processes by transforming complex resource constraints into automated, agile operational intelligence for AI feature evaluation.

What you'll do

  • Design and implement predictive forecasting and capacity allocation models to project evaluation demand and workforce utilization.
  • Build and maintain resilient data pipelines and operational telemetry to serve as a single source of truth for workflows.
  • Develop scalable decision-support tooling and simulation capabilities to help leads evaluate trade-offs and optimize spend.
  • Create intelligent, conversational automation and tool-calling workflows to turn manual triage into self-service actions.
  • Establish monitoring and anomaly detection systems to proactively identify pipeline bottlenecks and SLA risks.
  • Translate fragmented operational data streams into unified data models and actionable decision tools.

What we're looking for

  • 3–5+ years of industry experience in applied science, data engineering, or operations research.
  • Strong programming proficiency in Python and SQL for designing and maintaining resilient data pipelines.
  • Experience developing quantitative or predictive models for time-series forecasting, resource allocation, or constrained optimization.
  • Experience building intelligent automation, conversational interfaces, or tool-calling workflows using modern LLM APIs.
  • Demonstrated ability to translate fragmented data into unified models and communicate technical concepts 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 annotation pipelines, or AI/ML features evaluation workflows (preferred).
  • Experience designing internal tools using frameworks like FastAPI or Streamlit and familiarity with workflow orchestrators like Airflow (preferred).

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