Senior Data Scientist

The Oracle Cloud Infrastructure Data Science and Analytics team is seeking a passionate, experienced Senior Data Scientist to tackle complex technical challenges of the Infrastructure Build processes, building sophisticated analytical solutions to drive optimization and efficiency. The candidate should have deep expertise in researching disparate data sources, setting up large-scale data pipelines, and deploying large language models in OCI's growing Infrastructure space. As OCI continues to grow the customer base and regions, we need hands-on scientists who can lead data-driven initiatives to ensure that OCI's Infrastructure is in-tune to support new regions/ expansions. The ideal candidate is someone who thrives in the face of ambiguity and can quickly distill abstract ideas into concrete solutions and has a proven track record of delivering data-driven solutions across a highly complex environment involving multiple organizations, departments and teams.
The Senior Data Scientist will be responsible for statistical analyses, ML/ large language model deployment, forecasting models and lead business intelligence initiatives that impact Infrastructure and Networking for OCI. In addition, this specific role requires a passion for solving high-impact problems and the ability to jump into any in-flight project and get it on the rails.
Disclaimer:
Certain U.S. based or U.S. customer or client-facing roles may be required to comply with applicable requirements, such as immunization/occupational health mandates, and/or drug testing requirements.
Range and benefit information provided in this posting are specific to the stated locations only
US: Hiring Range in USD from: $100,700 to $209,500 per annum. May be eligible for bonus and equity.
Oracle maintains broad salary ranges for its roles in order to account for variations in knowledge, skills, experience, market conditions and locations, as well as reflect Oracle's differing products, industries and lines of business.
Candidates are typically placed into the range based on the preceding factors as well as internal peer equity.
Oracle US offers a comprehensive benefits package which includes the following:
1. Medical, dental, and vision insurance, including expert medical opinion
2. Short term disability and long term disability
3. Life insurance and AD&D
4. Supplemental life insurance (Employee/Spouse/Child)
5. Health care and dependent care Flexible Spending Accounts
6. Pre-tax commuter and parking benefits
7. 401(k) Savings and Investment Plan with company match
8. Paid time off: Flexible Vacation is provided to all eligible employees assigned to a salaried (non-overtime eligible) position. Accrued Vacation is provided to all other employees eligible for vacation benefits. For employees working at least 35 hours per week, the vacation accrual rate is 13 days annually for the first three years of employment and 18 days annually for subsequent years of employment. Vacation accrual is prorated for employees working between 20 and 34 hours per week. Employees working fewer than 20 hours per week are not eligible for vacation.
9. 11 paid holidays
10. Paid sick leave: 72 hours of paid sick leave upon date of hire. Refreshes each calendar year. Unused balance will carry over each year up to a maximum cap of 112 hours.
11. Paid parental leave
12. Adoption assistance
13. Employee Stock Purchase Plan
14. Financial planning and group legal
15. Voluntary benefits including auto, homeowner and pet insurance
The role will generally accept applications for at least three calendar days from the posting date or as long as the job remains posted.
As part of Oracle's onboarding process and consistent with applicable law, US-based employees are required to complete identity verification, which involves the collection and processing of their biometric information. Accommodations to this requirement may be granted following an individualized assessment.
RESPONSIBILITIES
What You’ll Do
Lead exploratory research and rigorous statistical analysis to translate complex data into clear, actionable answers to strategic business questions; apply data transformation, experimental design, predictive modeling, and machine-learning methods as appropriate.
Design, fine-tune, and optimize scalable algorithms and models, ensuring strong reliability, performance, reproducibility, and operational readiness in high-volume production environments.
Partner with data scientists, engineers, product teams, and business stakeholders to define data requirements, evaluate analytical opportunities, and deliver solutions aligned with measurable business outcomes.
Establish and champion data-quality standards, including validation, monitoring, lineage, and remediation practices, recognizing that reliable inputs are foundational to trustworthy analytical and ML outputs.
Mentor junior data scientists on statistical rigor, modeling best practices, experiment evaluation, code quality, and effective communication of data-driven findings.
Lead the development, evaluation, and fine-tuning of LLM-based solutions for infrastructure-build and data-center-materials use cases, including domain adaptation, retrieval/evaluation strategies, and performance measurement.
Stay current on advances in statistics, machine learning, and data science; assess and apply emerging methods to improve business processes, product capabilities, and decision quality.
Stay up-to-date with the latest developments in machine learning, statistics, and data science, applying new techniques for process/ product improvements.
Minimum Qualifications
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, quantify uncertainty, 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.