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About This Role
Oracle Cloud Infrastructure (OCI) is seeking a highly motivated Principal Data Automation Engineer to help accelerate the digital transformation of OCI's global supply chain operations. Reporting to the Senior Director of Data Automation, Supply Chain, this role will design, develop, and implement advanced data platforms, AI\-enabled automation, predictive analytics, and operational intelligence solutions that improve efficiency, scalability, and decision\-making across OCI's rapidly growing supply chain organization.
As a senior individual contributor, you will partner closely with Supply Planning, Manufacturing, Sourcing, Logistics, Capacity Planning, Data Center Operations, and Engineering teams to develop intelligent automation, operational dashboards, machine learning models, and data products that enable proactive management of supply chain operations. This role requires deep technical expertise, strong business acumen, and the ability to influence cross\-functional stakeholders through data\-driven solutions.
Data Automation \& AI Solutions
- Design, build, and maintain scalable data automation solutions supporting OCI's global supply chain.
- Develop AI and machine learning models that improve forecasting, inventory optimization, supply planning, logistics execution, manufacturing readiness, and operational performance.
- Identify opportunities to eliminate manual processes through automation, predictive analytics, and intelligent workflows.
- Build reusable automation frameworks and data products that improve operational efficiency and business scalability.
- Evaluate emerging AI and automation technologies and recommend practical applications across supply chain operations.
Data Engineering \& Analytics
- Design and develop robust data pipelines, models, and architectures that support real\-time operational reporting and advanced analytics.
- Build scalable datasets that enable forecasting, planning, inventory management, supplier performance, and deployment execution.
- Ensure data quality, governance, reliability, and accessibility across multiple enterprise systems.
- Develop dashboards, scorecards, and self\-service analytics that improve operational visibility across global supply chain functions.
- Collaborate with engineering teams to integrate data across Oracle Fusion Cloud Applications, operational systems, and cloud platforms.
Operational Intelligence
- Develop operational dashboards, KPI frameworks, and control tower capabilities that provide end\-to\-end visibility into supply chain performance.
- Create intelligent alerting mechanisms that proactively identify operational risks, exceptions, and bottlenecks.
- Build predictive models supporting scenario planning, capacity management, supplier performance, and deployment readiness.
- Translate complex operational data into actionable insights that support day\-to\-day execution and long\-term planning.
Cross\-Functional Collaboration
- Partner with Supply Planning, Procurement, Manufacturing, Logistics, Capacity Management, and Data Center Operations teams to understand business challenges and develop scalable technical solutions.
- Collaborate with product managers, engineers, and business stakeholders to define analytics requirements and deliver impactful data solutions.
- Provide technical guidance and subject matter expertise for automation initiatives and enterprise data projects.
- Influence best practices for data engineering, analytics, and automation across the organization.
Continuous Improvement
- Drive improvements in data quality, automation, reporting accuracy, and operational efficiency.
- Identify opportunities to simplify processes, reduce technical debt, and improve maintainability of data platforms.
- Document technical designs, data models, and automation solutions to support long\-term scalability and operational excellence.
- Stay current on emerging technologies in AI, machine learning, cloud computing, and data engineering.
Required Qualifications
Experience
- 7–10\+ years of experience in data engineering, analytics, automation, AI/ML, or related technical roles.
- Experience designing and developing scalable data pipelines, analytics platforms, and automation solutions.
- Proven experience delivering data products and dashboards supporting cross\-functional business operations.
- Experience applying predictive analytics or machine learning to solve operational or supply chain challenges.
- Demonstrated ability to independently lead complex technical initiatives from concept through implementation.
- Experience supporting supply chain, manufacturing, logistics, procurement, or cloud infrastructure operations is preferred.
Technical Skills
- Strong proficiency in SQL and Python for data engineering, automation, and analytics.
- Experience with ETL/ELT pipelines, data modeling, orchestration frameworks, and cloud\-based data platforms.
- Knowledge of AI/ML techniques, predictive analytics, and automation frameworks.
- Experience with business intelligence and visualization tools such as Oracle Analytics Cloud, Tableau, or Power BI.
- Familiarity with Oracle Fusion Cloud Applications (SCM, Procurement, Planning, ERP) is preferred.
- Understanding of forecasting, inventory management, manufacturing operations, logistics, and supply planning concepts.
- Experience working with cloud technologies and distributed systems architectures is a plus.
Professional Skills
- Strong analytical and problem\-solving abilities with attention to detail.
- Excellent communication skills with the ability to explain technical concepts to both technical and business audiences.
- Ability to manage multiple priorities in a fast\-paced, rapidly evolving environment.
- Demonstrated ability to influence stakeholders and drive technical solutions without direct managerial authority.
- Self\-motivated with a passion for automation, continuous improvement, and innovation.
Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 3,708 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Oracle, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $192,890 based on 463 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Oracle AI Hiring
Oracle has 15 open AI roles right now. They're hiring across AI Agent Developer, AI Engineering Manager, AI/ML Engineer, AI Software Engineer. Positions span US, Seattle, WA, US, Redwood City, CA, US.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 median).
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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