Senior Data Scientist

Oracle Oracle · Enterprise · Nashville, TN +1

Senior Data Scientist at Oracle Cloud Infrastructure focusing on building and deploying LLM-based solutions for infrastructure optimization. Responsibilities include statistical analysis, ML model deployment, leading data-driven initiatives, and mentoring junior scientists. The role involves fine-tuning and optimizing algorithms, establishing data quality standards, and evaluating LLM solutions for business use cases.

What you'd actually do

  1. 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.
  2. Design, fine-tune, and optimize scalable algorithms and models, ensuring strong reliability, performance, reproducibility, and operational readiness in high-volume production environments.
  3. 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.
  4. 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.
  5. Mentor junior data scientists on statistical rigor, modeling best practices, experiment evaluation, code quality, and effective communication of data-driven findings.

Skills

Required

  • SQL
  • Python or R
  • data warehousing
  • structured and semi-structured data at scale
  • statistical rigor
  • modeling best practices
  • experiment evaluation
  • code quality
  • effective communication of data-driven findings
  • probability
  • statistical inference
  • experimental design
  • hypothesis testing
  • regression
  • predictive modeling
  • large, complex datasets
  • quantify uncertainty
  • actionable insights
  • data extraction
  • data transformation
  • analysis
  • visualization
  • reproducible modeling workflows
  • developing, validating, and deploying machine-learning models
  • model selection
  • feature engineering
  • performance evaluation
  • monitoring
  • evaluating and adapting LLM or generative-AI solutions
  • prompt design
  • benchmarking
  • quality assessment
  • Collaborative problem-solving
  • develop, evaluate, and iterate on analytical solutions in an ambiguous environment

Nice to have

  • cloud infrastructure and networking domain
  • MLOps
  • building workflows for model retraining, monitoring and deploying
  • working with ambiguous problem and driving it to the finish line

What the JD emphasized

  • deploying large language models
  • lead data-driven initiatives
  • statistical analyses
  • ML/ large language model deployment
  • lead business intelligence initiatives
  • lead exploratory research
  • rigorous statistical analysis
  • apply data transformation, experimental design, predictive modeling, and machine-learning methods
  • Design, fine-tune, and optimize scalable algorithms and models
  • Establish and champion data-quality standards
  • Mentor junior data scientists
  • Lead the development, evaluation, and fine-tuning of LLM-based solutions
  • domain adaptation, retrieval/evaluation strategies, and performance measurement
  • Stay current on advances in statistics, machine learning, and data science
  • apply emerging methods to improve business processes, product capabilities, and decision quality
  • Experience evaluating and adapting LLM or generative-AI solutions for business use cases
  • prompt design, benchmarking, and quality assessment

Other signals

  • deploying large language models
  • lead data-driven initiatives
  • statistical analyses
  • ML/ large language model deployment
  • lead business intelligence initiatives
  • lead exploratory research
  • rigorous statistical analysis
  • apply data transformation, experimental design, predictive modeling, and machine-learning methods
  • Design, fine-tune, and optimize scalable algorithms and models
  • Establish and champion data-quality standards
  • Mentor junior data scientists
  • Lead the development, evaluation, and fine-tuning of LLM-based solutions
  • domain adaptation, retrieval/evaluation strategies, and performance measurement
  • Stay current on advances in statistics, machine learning, and data science
  • apply emerging methods to improve business processes, product capabilities, and decision quality
  • Experience evaluating and adapting LLM or generative-AI solutions for business use cases
  • prompt design, benchmarking, and quality assessment