This role pays more than
70%
of similar roles. Most pay
$126,800–$195,312
— the shaded band above.
At the midpoint, this role pays about
$181k
versus about
$161k
for comparable roles.
Based on 240 similar postings.
Employer
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
2315 open roles
on FindRole.
Listed pay typically runs
$175,000–$277,600
across 1885 roles with salary data.
Data Scientist, US Decision Intelligence joins the US Decision Intelligence team to build and scale automated insight pipelines that power the sales organization. You will develop machine learning models for forecasting, anomaly detection, attribution modeling, and causal inference while implementing LLM prompt engineering to translate complex data into actionable insights for internal stakeholders. The role involves building recommendation engines, evaluating GenAI tools for accuracy and latency, and performing A/B testing on new insight types. You will utilize Python, SQL, and various data science libraries alongside cloud platforms like Snowflake or BigQuery. Essential skills include experience with RAG architectures, vector databases, and visualization tools such as Tableau or Plotly. This role serves as a technical translator, bridging the gap between engineering teams and business stakeholders to solve problems related to sales performance and automated decision-making.
What does a Data Scientist earn in California?
Median $214000 from 74 postings across 27 companies.
Build and scale automated insight pipelines using ML models to detect opportunities and diagnose performance issues.
Develop and deploy machine learning models for forecasting, anomaly detection, attribution modeling, and causal inference.
Create root cause analysis and recommendation engines to enhance chatbot capabilities and summary tools.
Implement LLM evaluation pipelines to measure accuracy, latency, and user satisfaction of AI agents.
Perform prompt engineering to translate complex data findings into actionable insights for sales teams.
Conduct A/B testing and experimentation to validate new insight types and interaction methods.
Translate complex business problems into technical solutions while communicating results to non-technical stakeholders.
Influence upstream data model designs and define key performance indicators (KPIs) for the sales organization.
What we're looking for
Bachelor's degree in Computer Science, Statistics, Mathematics, Engineering, Economics, Applied Mathematics, Machine Learning, or a related field.
Advanced degree (MS or Ph.D.) in Economics, Electrical Engineering, Statistics, Data Science, or a similar quantitative field (preferred).
4+ years of experience in a Data Science, Data Analysis, or Data Visualization role.
Hands-on experience with LLMs, RAG architectures, and prompt engineering.
Proficiency in Python, SQL, and ML/data science libraries.
Applied knowledge of statistical data analysis, predictive modeling, classification, Time Series techniques, sampling methods, multivariate analysis, hypothesis testing, and drift analysis.
Experience with data visualization tools (Tableau, d3, plotly) and cloud data platforms like Snowflake, Spark, or BigQuery.
Familiarity with deployment frameworks (Docker, Kubernetes, FastAPI), Git workflows, and production environments.
Production experience with GenAI frameworks, LLM observability tools, vector databases, and MLOps practices (preferred).