Sales AI Data Scientist

Job description

Imagine what you could do here.

At Apple, new ideas have a way of becoming outstanding products, services, and customer experiences very quickly.

Bring passion and dedication to your job, and there's no telling what you could accomplish.

Apple's Sales organization generates the revenue needed to fuel our ongoing development of products and services.

This, in turn, enriches the lives of hundreds of millions of people around the world.

We are, in many ways, the face of Apple to our largest customers.

Apple's Decision Intelligence (DI) team is looking for a versatile individual who is passionate about crafting, implementing, and operating analytical solutions that have a direct and measurable impact on Apple Sales and its customers.

Description

As a DI Data Scientist, you will employ predictive modeling, data visualization, and statistical analysis techniques to build end-to-end solutions for internal collaborators, using sales performance data, market data, programs, external data, etc.

This role will operate in both capacities, to augment existing data solutions, as well as innovate and inventing data science projects, crafting analytic experiences that simplify data into insights and catalyze decision-making.

Analytics is a team sport, and in your role, you will be key in leading and influencing teams on the translation of business problems and questions into data science models.

Responsibilities

• In this role, you will:
• Be a core technical contributor building models and intelligence layers that power our AI agents, insights engines, and GenAI-enhanced platforms.
  
  You'll develop robust ML pipelines, design evaluators for LLM responses, and embed decision-making intelligence into sales-facing tools.
  
  Your work will drive prescriptive analytics, root cause analysis, and agent behavior tuning.
  
  You will do all this by:
• Developing and productionizing ML models (e.g., forecasting, anomaly detection, attribution, causal inference) used by our AI agents and insights platforms
• Building RCA and recommendation engines that enhance summarization and chatbot capabilities.
  
  
• Analyzing agent interactions and implementing LLM evaluation pipelines to measure factual accuracy, latency, and user satisfaction.
  
  
• Supporting experimentation and A/B testing for new insight types and interaction methods.
  
  
• Partnering with AI engineers and PMs to scale features across regions and tools.
  
  
• Act as a data translator, bridging the gap in expertise between technical teams, made up of data analysts, data engineers, software developers, and business stakeholders.
  
  Successfully bridging analytics and business, with the ability to speak the language of both.
  
  
• Influence upstream data model design, drive benchmark definitions, and develop your own data solutions as needed.
  
  
• Establish a comprehensive roadmap to communicate and manage our commitments and stakeholder expectations while enabling org-wide visibility on progress.
  
  
• Build and support dashboards and self-service tools (using several platforms) to analyze and present internal and external data.
  Leverage available AI/ML models across the company and translate those for region-specific needs.
  
  

Minimum Qualifications

• We're looking for someone with an eagerness and ability to learn new skills and solve dynamic problems in an encouraging and expansive environment.
  
  
• Familiarity with vector similarity search, RAG architectures, and LLM prompt evaluation.
  
  
• Experience co-developing with software engineers in production environments.
  
  
• Ability to lead development projects from start to finish.
  
  
• Comfort with ambiguity.
  
  Ability to structure complex analysis through data analysis and strategy research.
  
  
• Collaborate closely with business teams to deep dive into business performance and improve reporting dashboards on key operational metrics.
  
  
• 4+ years of experience in a Data Visualization, Data Science, Data Analysis, or Data Translation role, with a keen eye for design and attention to detail.
  
  
• Applied knowledge of statistical data analysis, predictive modeling classification, Time Series techniques, sampling methods, multivariate analysis, hypothesis testing, and drift analysis.
  
  
• Proficiency in SQL and experience with at least one major data analytics platform, such as Hadoop, Spark, or Snowflake.
  
  
• Expertise with data visualization tools (such as Tableau, d3, plotly, etc.) for data analysis and presentation.
  
  Experience with Tableau Server, TabPy, and Extensions is a plus.
  
  
• Proficiency in programming languages, tools, and frameworks like Python, Git, Notebooks, Dataiku, and Streamlit.
  
  
• Knowledge of project management and productivity tools such as Wrike, Sketch.
  
  
• Strong time management skills with the ability to collaborate across multiple teams.
  
  
• Knowledge of best practices in data analysis, data visualization, and data science.
  
  
• Able to balance competing priorities, long-term projects, and ad hoc requirements.
  
  
• Ability to work in a fast-paced, dynamic, constantly evolving business environment.
  
  
• Bachelors's degree in Computer Science, Statistics, Mathematics, Engineering, Economics, Applied Mathematics, Machine Learning, or a related field.
  
  

Preferred Qualifications

• Experience with observability tools for LLMs (e.g., LangSmith, Truera, Weights & Biases)
• Proven experience working with LLMs and GenAI frameworks (LangChain, LlamaIndex, etc.)
• Strong experience articulating and translating business questions into data solutions.
  
  
• Communicate results and insights effectively to partners and senior leaders, as well as both technical and non-technical audiences.
  
  
• Experience with anomaly detection and causal inference models.
  
  
• Sound communication skills - adept at messaging domain and technical content, at a level appropriate for the audience.
  
  Strong ability to gain trust with stakeholders and senior leadership.
  
  
• Familiarity with embedding, retrieval algorithms, agents, and data modeling for vector development graphs.
  Advanced Degree (MS or Ph.D.) in Economics, Electrical Engineering, Statistics, Data Science, or a similar quantitative field.
  
  

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