below marketmost similar roles pay hereabove market
This role pays less than
55%
of similar roles. Most pay
$126,800–$189,187
— the blue band above.
At the midpoint, this role pays about
$155k
versus about
$158k
for comparable roles.
Based on 240 similar postings.
Employer
About Oracle
Oracle Corporation is a leading multinational technology company specializing in database software, cloud computing, and enterprise software.
Oracle currently has
955 open roles
on FindRole.
Listed pay typically runs
$102,300–$234,600
across 790 roles with salary data.
The Senior Data Scientist joins the Oracle Cloud Infrastructure Data Science and Analytics team to address complex technical challenges within Infrastructure Build processes. This role involves building sophisticated analytical solutions to drive optimization and efficiency across the growing Infrastructure space. Key responsibilities include leading exploratory research, performing rigorous statistical analysis, and designing scalable algorithms for high-volume production environments. The successful candidate will develop and fine-tune large language models for infrastructure-build and data-center-materials use cases while establishing data-quality standards. Required technical skills include proficiency in SQL, Python, or R, along with experience in data warehousing, predictive modeling, and machine learning. The role specifically solves business problems related to infrastructure expansion, networking, and forecasting models to ensure the cloud infrastructure supports new regions and global growth.
What does a Data Scientist earn?
Median $173200 from 338 postings across 66 companies.
Lead exploratory research and statistical analysis to translate complex data into actionable business answers.
Design, fine-tune, and optimize scalable machine learning algorithms for high-volume production environments.
Develop, evaluate, and fine-tune LLM-based solutions for infrastructure-build and data-center-materials use cases.
Establish and champion data-quality standards including validation, monitoring, lineage, and remediation practices.
Mentor junior data scientists on statistical rigor, modeling best practices, and code quality.
Build and manage large-scale data pipelines to support infrastructure optimization and efficiency.
Apply emerging machine learning and statistical methods to improve business processes and product capabilities.
What we're looking for
Applicants must have 3 to 5+ years of experience.
Applicants must be able to read, write, and speak English.
Bachelor’s degree or equivalent practical experience in Data Science, Statistics, Computer Science, Applied Mathematics, Engineering, Economics, or a related quantitative field.
Strong foundation in probability, statistical inference, experimental design, hypothesis testing, regression, and predictive modeling.
Experience applying rigorous statistical analysis to large, complex datasets to answer business questions and communicate actionable insights.
Proficiency in SQL and Python or R for data extraction, transformation, analysis, visualization, and reproducible modeling workflows.
Experience with data warehousing and working with structured and semi-structured data at scale.
Hands-on experience developing, validating, and deploying machine-learning models, including model selection, feature engineering, performance evaluation, and monitoring.
Experience evaluating and adapting LLM or generative-AI solutions for business use cases, including prompt design, benchmarking, and quality assessment.
Experience with cloud infrastructure and networking domain (preferred).
Experience with MLOps, building workflows for model retraining, monitoring and deploying (preferred).
Experience working with ambiguous problems and driving them to the finish line (preferred).
Python
SQL
Machine Learning
Generative AI
Large Language Models
LLMs
Statistical Analysis
Predictive Modeling
Causal Inference
AI Agents
Data Science
Experimentation
Model Evaluation
Production Monitoring
Recommendation Systems
Segmentation
Forecasting
Media Mix Modeling
Multi Touch Attribution
SQL
Python
R
Databricks
BigQuery
Machine Learning
Multivariate Regression
Optimization Algorithms
Generative AI
RShiny
Ads Data Hub
Data Science
Machine Learning
Python
R
SQL
Statistical Analysis
Data Pipelines
Feature Engineering
Causal Inference
Experimentation Frameworks
Model Monitoring
Responsible AI
Data Quality
Data Science
Optimization