Data Scientist, Decision Intelligence — AI Insights

Apple Inc.; Cupertino (CA); On-site; USD 145,000 - 218,000; Full time

Benefits offered by this job

Medical & dental coverage

Retirement benefits

Employee stock programs

Tuition reimbursement

Job summary

Apple Inc. is seeking a Data Scientist for the US Decision Intelligence team in Cupertino. You will build and scale automated insight pipelines powering Apple’s sales organization, developing ML models for forecasting, anomaly detection, and causal inference.

You will translate business problems into technical solutions, evaluate LLMs, and embed insights into dashboards and AI-enabled tools used by sales teams. A strong collaboration across data engineers and PMs is essential.

Qualifications

• 4+ years in Data Science or related roles.
• Hands-on with LLMs, RAG, and prompt engineering.
• Strong Python and ML libraries.
• Statistics, predictive modeling, time series, hypothesis testing.

Responsibilities

• Build and scale automated insight pipelines for the sales org.
• Develop ML models for forecasting, anomaly detection, and attribution.
• Create RCA and recommendation engines for summaries and chatbots.
• Evaluate LLMs for factual accuracy and latency.
• Collaborate with AI engineers and PMs to scale features.

Skills

LLMs

Python

SQL

ML/DLL libraries

Data Visualization

Git

Prompt engineering

Docker/Kubernetes

Education

Bachelor’s degree in CS/Stats/Math/Engineering

Advanced degree (MS/PhD) preferred

Tools

Tableau Server

TabPy/Extensions

LangChain/LlamaIndex

Weigths & Biases / TruLens

Spark / BigQuery / Snowflake

Job description

Apple Inc. is seeking a Data Scientist for the US Decision Intelligence team in Cupertino. You will build and scale automated insight pipelines powering Apple’s sales organization, developing ML models for forecasting, anomaly detection, and causal inference.

You will translate business problems into technical solutions, evaluate LLMs, and embed insights into dashboards and AI-enabled tools used by sales teams. A strong collaboration across data engineers and PMs is essential.