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Data ScientistApple • United States
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Data Scientist

Apple
  • US
    United States
  • US
    United States
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Über

Data Scientist
At Apple, we believe extraordinary products are built through deep understanding, rigorous analysis, and relentless focus on quality. We are seeking an exceptional Data Scientist to lead algorithm evaluation and performance intelligence for next-generation intelligent systems. In this highly visible technical role, you will define how algorithm quality is measured, understood, and improved. You will drive evaluation methodologies, establish scalable metrics frameworks, and lead deep technical investigations into algorithm behavior, failure modes, and system performance. Working at the intersection of machine learning, data science, and product quality, you will influence critical decisions through data-driven insights and technical leadership. You will collaborate closely with algorithm engineers, machine learning researchers, QA, annotation teams, and cross-functional partners to shape evaluation strategy and improve the robustness, reliability, and customer experience of intelligent systems at scale. This role also requires identifying opportunities to leverage agentic systems and AI-assisted workflows to improve efficiency, scalability, and technical depth in evaluation and analysis. As a Data Scientist focused on Algorithm Evaluation, you will serve as a technical leader responsible for driving end-to-end evaluation strategy for complex algorithmic systems. You will develop rigorous methodologies to assess algorithm quality, identify failure patterns, and quantify system behavior across large-scale datasets and real-world scenarios. You will lead deep dives into algorithm performance, uncover insights through advanced statistical analysis, and establish scalable frameworks to improve evaluation efficiency and confidence in product decisions. You will also help shape how agentic solutions and AI-assisted tooling are integrated into day-to-day workflows to accelerate data analysis, failure investigation, annotation quality improvement, root-cause discovery, and evaluation automation. This role requires strong technical depth, exceptional analytical rigor, and the ability to influence cross-functional teams in highly ambiguous environments. Responsibilities
Define and drive evaluation strategy, methodologies, and success metrics for machine learning and algorithmic systems across multiple product areas. Establish scalable frameworks for measuring algorithm quality, robustness, reliability, and customer impact. Lead deep technical investigations into algorithm performance, model behavior, regression trends, and failure patterns using large-scale data analysis. Develop quantitative metrics and evaluation standards to assess algorithm quality across precision, recall, latency, robustness, edge cases, and real-world performance. Drive root-cause analysis of algorithm failures and partner with engineering teams to identify optimization opportunities and performance tradeoffs. Influence algorithm roadmap and product decisions through rigorous experimentation, statistical analysis, and actionable insights. Design and implement automated evaluation pipelines, benchmarking systems, visualization tools, and scalable reporting infrastructure. Identify opportunities to leverage agentic systems, LLM-based workflows, and AI-assisted tooling to improve efficiency and quality in evaluation, data analysis, annotation, and failure investigation. Develop intelligent workflows that utilize AI agents for tasks such as failure pattern clustering, anomaly detection, data curation, evaluation synthesis, experiment analysis, and large-scale reporting. Drive adoption of AI-assisted solutions to reduce manual effort in data deep dives, regression triage, and annotation quality analysis. Partner closely with algorithm engineers, ML researchers, QA, and annotation teams to improve evaluation coverage, data quality, and operational efficiency. Lead cross-functional efforts to establish best practices for algorithm validation, regression analysis, evaluation governance, and AI-assisted evaluation workflows. Mentor team members and raise technical standards in evaluation methodologies, statistical rigor, and analytical best practices. Minimum Qualifications
BS and a minimum of 10 years relevant industry experience 7+ years of experience in data science, machine learning evaluation, algorithm analysis, or related technical disciplines. Demonstrated experience driving technical initiatives in ambiguous, cross-functional environments. Strong expertise in statistical analysis, experimentation methodologies, and large-scale data analytics. Deep experience evaluating machine learning, computer vision, or AI systems through quantitative metrics and performance analysis. Strong programming experience in Python, with hands-on experience building scalable analytics and automation pipelines. Experience conducting algorithm deep dives, failure analysis, and model performance investigations. Familiarity with AI-assisted analysis workflows, foundation models, agentic systems, or intelligent automation approaches for technical problem solving. Strong understanding of algorithm evaluation concepts, including precision/recall tradeoffs, confusion analysis, robustness measurement, regression detection, and benchmarking methodologies. Exceptional problem-solving skills with ability to translate ambiguous technical problems into measurable frameworks. Preferred Qualifications
Experience evaluating machine learning, computer vision, multimodal, or foundation model systems in production environments. Experience designing or deploying agentic workflows to improve engineering productivity, data analysis, evaluation efficiency, or annotation quality. Familiarity with LLM-based systems, retrieval pipelines, structured reasoning, or AI-assisted analytics frameworks. Experience defining quality frameworks and evaluation methodologies for large-scale intelligent systems. Experience building automated benchmarking systems and large-scale performance monitoring infrastructure. Knowledge of A/B experimentation, causal inference, and advanced statistical modeling. Strong understanding of the ML lifecycle, model validation, and continuous evaluation methodologies. Excellent communication skills with proven ability to influence technical decisions through data-driven insights. Pay & Benefits
At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $184,700 and $324,800, and your base pay will depend on your skills, qualifications, experience, and location. Apple employees also have the opportunity to become an Apple shareholder through participation in Apple's discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple's Employee Stock Purchase Plan. You'll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses — including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation. Learn more about Apple Benefits Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program. Apple is an equal opportunity employer that is committed to inclusion and diversity. We seek to promote equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics. Learn more about your EEO rights as an applicant At Apple, we believe accessibility is a fundamental human right. You'll find that idea reflected in everything here — in our culture, our benefits and our digital tools. By welcoming as many perspectives as possible, we help you build a career where you feel like you belong. Learn about accessibility in Apple's workplace Learn about reasonable accommodations for job applicants Apple accepts applications to this posting on an ongoing basis.
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