Data Scientist, Evaluation

Apple’s Evaluation organization is hiring a Sr Data Scientist to develop statistical and LLM-augmented analytical models for online metrics and insights, supporting the evolution of Apple AI products.

Responsibilities

• Apply statistical and LLM analysis to structured and linguistic data to improve understanding of Apple AI and help shape future versions.
• Investigate world-wide product usage to surface weaknesses and opportunities to improve product performance.
• Define metrics and instrumentation strategies to evaluate the success of new features.
• Partner with Engineering leads and product partners to advance a privacy-first approach to analytical insight.
• Use a range of analytical approaches, methodologies, frameworks, and technical strategies, with an emphasis on language data analysis.
• Demonstrate structured, critical thinking to build partner credibility and communicate insights clearly.
• Develop conceptual and analytical models using LLM-augmented analytics and Python, then run insight analyses to identify product quality issues that can improve the user experience.
• Contribute to product understanding and development of Apple features using analytics and engineering methods.

Requirements

• 3+ years of data science experience applying quantitative methods to structured and unstructured data for exploratory data analysis, pattern recognition, metrics development, and analytical insights.
• Strong programming skills in Python and SQL, including data manipulation, processing, and building analytics pipelines.
• Experience with LLM-driven analytics workflows, including Python coding frameworks and evaluation pipelines.
• Proven ability to analyze user behavior, design and interpret A/B experiments, and translate results into product improvements.
• Master degree in a technical or quantitative field such as Statistics, Mathematics, Computer Science, Engineering, Linguistics, or Physics (PhD preferred).
• Experience with Natural Language Processing (NLP) and LLMs.
• Experience developing dataset and analysis conceptual methodologies.
• Self-motivated and curious with demonstrated creative and critical thinking and a drive to determine and improve how things work.
• Excellent communication skills to synthesize analyses into clear insights and influence cross-functional partners.
• High tolerance for ambiguity, with the ability to navigate uncertainty and synthesize connections.
• Demonstrated experience encouraging partner relationships and influencing decisions with data.

Technologies

• Python
• SQL
• LLMs
• Natural Language Processing (NLP)

Location

• Cupertino, CA (onsite)

Pay & Benefits

• Base pay range: USD 150,400 - 277,600 per year.
• Base pay depends on skills, qualifications, experience, and location.
• Apple benefits, compensation, and employee stock programs are subject to eligibility requirements and plan or program terms.

Benefits

• Comprehensive medical and dental coverage
• Retirement benefits
• Discounted products and free services
• Reimbursement for certain educational expenses, including tuition
• Discretionary bonuses or commission payments and relocation (eligibility-dependent)
• Opportunity to become an Apple shareholder through participation in Apple’s discretionary employee stock programs
• Eligible for discretionary restricted stock unit awards
• Can purchase Apple stock at a discount if participating in the Employee Stock Purchase Plan

Minimum Qualifications

• 3+ years of data science experience applying quantitative methods to structured and unstructured data for exploratory data analysis, pattern recognition, metrics development, and analytical insights.
• Strong programming in Python and SQL, with data manipulation, processing, and analytics pipelines.
• Experience with LLM-driven analytics workflows using Python coding frameworks and evaluation pipelines.
• Ability to analyze user behavior, design and interpret A/B experiments, and translate findings into product improvements.
• Master degree in a technical or quantitative field (PhD preferred).