This role pays less than
71%
of similar roles. Most pay
$185,500–$237,787
— the shaded band above.
At the midpoint, this role pays about
$195k
versus about
$212k
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
242 open roles
on FindRole.
Listed pay typically runs
$102,300–$209,500
across 225 roles with salary data.
Principal Applied Scientist joins the Oracle Health Data Intelligence team to develop scalable machine learning and AI solutions for healthcare organizations. This hands-on technical leadership role involves leading the end-to-end development of production-ready models, including problem formulation, data analysis, feature engineering, and deployment. The candidate will build high-quality Python code and implement diverse techniques such as deep learning, NLP, information retrieval, ranking, forecasting, anomaly detection, and generative AI. Key tools include PyTorch, TensorFlow, JAX, scikit-learn, and Spark. The role focuses on solving complex problems in the healthcare domain by creating robust systems for clinical impact while ensuring safety, fairness, and reliability. You will collaborate with cross-functional partners to translate real-world needs into durable capabilities, mentor other scientists, and establish monitoring workflows within a high-reliability, privacy-sensitive environment.
What does a Applied Scientist earn?
Median $208800 from 47 postings across 11 companies.
Lead the end-to-end development of machine learning solutions from problem formulation to deployment and monitoring.
Write high-quality, maintainable production code in Python rather than just creating research prototypes.
Apply diverse techniques including NLP, information retrieval, forecasting, and generative AI to solve business problems.
Develop evaluation frameworks to measure model quality, safety, fairness, and clinical impact.
Conduct rigorous experimentation including statistical analysis, error analysis, and root-cause investigations.
Partner with engineers to productionize models, optimize inference performance, and establish retraining workflows.
Translate complex requirements from product and clinical partners into durable AI capabilities.
Mentor other scientists and engineers through design reviews, code reviews, and technical guidance.
What we're looking for
PhD in Computer Science, Machine Learning, Statistics, Operations Research, or equivalent practical experience; OR a Master’s degree with 6+ years of relevant industry experience.
8+ years of experience applying machine learning, data science, or AI techniques to real-world product or business problems.
Strong programming ability in Python to write clean, efficient, and production-quality code with testing and documentation.
Experience building and shipping machine learning systems including data pipelines, training workflows, inference services, evaluation, and monitoring.
Deep understanding of core machine learning concepts such as supervised/unsupervised learning, optimization, and statistical inference.
Proficiency with ML frameworks such as PyTorch, TensorFlow, JAX, scikit-learn, or Spark.
Demonstrated ability to lead ambiguous technical projects and communicate effectively with both technical and non-technical audiences.
Experience with generative AI, LLMs, NLP, search, ranking, or high-reliability systems (preferred).
Generative AI
Machine Learning
Foundation Models
RAG
Multi-agent Architectures
Data Science
Predictive Analytics
Statistics
Observability
Responsible AI
Python
Machine Learning
Retrieval-Augmented Generation
Large Language Models
Data Engineering
OCI
knowledge-graph s
Data Pipelines
Statistical Analysis
Model Evaluation
Experimental Design
Bioinformatics
Genomics
Large Language Models
Information Retrieval
Ranking Systems
Agentic AI
Retrieval-Augmented Generation
Supervised Fine-Tuning
Preference Optimization
Model Distillation
Machine Learning
A/B Testing
Distributed Training
Search Engines
Machine Learning
Large Language Models (LLMs)
Deep Learning
Azure
AI Safety
Supervised Fine-Tuning
Distillation
Policy Optimization
Distributed Systems
Algorithms
Software Engineering
Predictive Analytics
Statistics
Data Science