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Senior Solution Consultant, Intelligent Analytics \& AI Who are we?
At Oracle NetSuite, our vision is to transform how businesses operate so they can achieve their business goals faster, smarter, and with greater confidence. As the world's leading cloud business management suite, NetSuite unifies ERP, financials, CRM, ecommerce, supply chain, planning, analytics, and emerging AI capabilities into a single platform that helps organizations adapt and thrive in a rapidly changing business environment.
Today, business leaders are looking beyond system modernization; they want platforms that help them understand what is happening across the business, predict outcomes, automate decisions, and unlock new growth. Through Oracle's investments in Artificial Intelligence, analytics, data, and business applications, we help customers reimagine how teams work, make decisions, and drive business performance.
We are passionate about innovation, customer success, and creating lifelong advocates for NetSuite. Every interaction is an opportunity to help our customers realize greater value from their technology investments and achieve outcomes that transform their businesses.
Who are we looking for?
We are looking for intellectually curious, business\-minded innovators who are passionate about helping organizations solve complex challenges through technology. The ideal candidate combines business analytics expertise, operational or financial acumen, strategic thinking, consultative selling skills, and enthusiasm for emerging technologies, including Artificial Intelligence and intelligent automation.
You thrive in dynamic environments, enjoy engaging with executives and business leaders, and are energized by helping customers operate more effectively. You are comfortable discussing reporting and decision\-making challenges with C\-level leaders, data analytics and AI\-driven transformation with business stakeholders, and solution architecture, data integration, and governance with technical stakeholders.
If you are motivated by continuous learning, innovation, customer impact, and the opportunity to shape the future of business applications and AI, this is the role for you.
Why is this an exciting opportunity?
As part of Oracle NetSuite's Sales Consulting Organization, the Senior Solution Consultant, Intelligent Analytics \& AI helps customers envision the future of intelligent business operations. You will engage directly with prospects, existing customers, and channel partners to understand objectives, identify transformation opportunities, and show how NetSuite Analytics Warehouse and AI\-powered solutions deliver measurable business value.
This role goes beyond traditional product demonstrations. You will serve as a trusted advisor, helping organizations modernize reporting, analytics, data warehousing, and decision\-making through trusted data models, AI\-assisted insights, natural language analytics, large language models, intelligent automation, and agentic workflows.
Working closely with executives, finance leaders, operational stakeholders, and data and IT teams, you will connect business challenges to strategic outcomes and help customers understand how Oracle solutions can change the way their organizations operate, compete, and grow.
This is a unique opportunity to be at the forefront of Oracle's AI strategy, influence high\-impact business decisions, and help define the next generation of intelligent analytics and AI\-enabled business decision\-making.
Responsibilities
As a subject matter expert, the Senior Solution Consultant, Intelligent Analytics \& AI provides strategic direction and specialized knowledge around NetSuite Analytics Warehouse, business intelligence, data integration, AI\-enabled analytics, and solutions related to a customer's business and industry.
You will partner with business leaders to reimagine how trusted data, analytics, and AI can improve processes for a customer's specific industry. Your objective is not only to demonstrate software functionality, but to help customers envision new ways of operating through insights, automation, and intelligent decision support.
You will create and facilitate a vision for customers using executive presentations, solution workshops, demonstrations, proof\-of\-concepts, and innovation sessions that bring a customer's vision to life.
As a Senior Solution Consultant, Intelligent Analytics \& AI, you will:
- Serve as a trusted advisor to CFOs, operations leaders, controllers, finance transformation executives, IT and data leaders, and line\-of\-business stakeholders.
- Lead discovery sessions focused on business objectives, reporting maturity, data quality, integration challenges, operational inefficiencies, and AI readiness.
- Design and deliver value\-based demonstrations that connect NetSuite Analytics Warehouse, Oracle Analytics, NetSuite ERP data, third\-party data sources, and AI capabilities to measurable business value.
- Translate customer requirements into solution strategies that use trusted data pipelines, curated metrics, semantic models, natural language analytics, embedded insights, and agentic workflows.
- Articulate the value of AI\-driven analytics transformation, including automated insight discovery, natural language query, narrative explanations, anomaly detection, forecasting support, and decision intelligence.
- Partner with Account Executives to develop business cases, executive\-level value propositions, and clear next steps tied to customer outcomes.
- Demonstrate Oracle NetSuite's vision for intelligent analytics through embedded AI, digital assistants, governed data access, approved AI platforms, APIs, connectors, and emerging MCP\-based integration patterns.
- Collaborate with Product Management and Engineering to provide market feedback, influence future NetSuite Analytics Warehouse and AI innovation, and develop thought leadership content focused on modern analytics and trusted data.
- Support strategic sales cycles through workshops, proof\-of\-concepts, solution architecture reviews, and executive briefings, while mentoring less experienced Solution Consultants through coaching and demonstration best practices.
Success in this role requires the ability to combine business analytics expertise, technology acumen, business consulting skills, and AI strategy to help organizations unlock greater agility, improve decision quality, and accelerate business performance.
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Oracle, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills in Demand for This Role
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000.
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
AI roles in Austin pay a median of $214,343 across 87 tracked positions.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
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).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
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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