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.
Internal Responsibilities
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.
Demonstrated ability to communicate analytical findings clearly to both technical and non-technical stakeholders.
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.
Collaborative problem-solving skills and the ability to develop, evaluate, and iterate on analytical solutions in an ambiguous environment.
Preferred Qualifications (Nice To Have)
- Experience with cloud infrastructure and networking domain
- Experience with MLOps, building workflows for model retraining, monitoring and deploying
- Experience working with ambiguous problem and driving it to the finish line
External Responsibilities
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.
Demonstrated ability to communicate analytical findings clearly to both technical and non-technical stakeholders.
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.
Collaborative problem-solving skills and the ability to develop, evaluate, and iterate on analytical solutions in an ambiguous environment.
Preferred Qualifications (Nice To Have)
- Experience with cloud infrastructure and networking domain
- Experience with MLOps, building workflows for model retraining, monitoring and deploying
- Experience working with ambiguous problem and driving it to the finish line
