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About This Role
The Simplot Company is a diverse, privately held global food and agriculture company headquartered in Boise, Idaho. We are a true farm-to-table company with an integrated portfolio including food processing and food brands, phosphate mining, fertilizer manufacturing, farming, ranching and cattle production, and other enterprises related to agriculture.
Summary
We are seeking a Senior Data Science Domain Analyst (Commercial, Planning & Supply Chain) to help drive the adoption of data science, machine learning, and AI across commercial, planning, and supply chain functions at Simplot.
This role sits at the intersection of business strategy, operations, and advanced analytics, translating complex business challenges into scalable data and AI solutions. You will partner with stakeholders across Global Food, AgriBusiness, and corporate functions to identify opportunities, define high-value use cases, and guide the development of analytics, machine learning, and agentic AI solutions that improve decision-making and business performance.
The ideal candidate combines strong business acumen with analytical expertise and has experience delivering custom applications and decision-support tools that complement enterprise platforms such as SAP, Salesforce, and other commercial systems.
This position is full-time onsite at the Simplot Headquarters in Boise, ID.
Key Responsibilities
Domain Leadership & Use Case Development
- Partner with commercial, sales, marketing, planning, supply chain, procurement, and customer-facing teams to identify and prioritize high-value opportunities.
- Translate business challenges into analytics, optimization, machine learning, and AI use cases.
- Define business value, success metrics, data requirements, and implementation approaches.
Cross-Functional Collaboration
- Act as a bridge between business stakeholders, data scientists, AI engineers, and technology teams.
- Align stakeholders around solution objectives, priorities, and expected outcomes.
- Drive adoption through effective communication and user engagement.
Analytics & Solution Design
- Analyze business processes, operational data, and decision workflows to uncover opportunities for improvement.
- Evaluate data quality, system capabilities, and integration requirements across enterprise platforms.
- Support rapid prototyping, experimentation, and business case development.
AI & Application Enablement
- Collaborate with technical teams to conceive and deliver predictive, prescriptive, optimization, and generative AI solutions.
- Help shape custom applications that extend and complement enterprise platforms such as SAP, Salesforce, planning systems, and commercial tools.
- Guide solution deployment into business processes to ensure sustained business value.
Portfolio & Value Management
- Assess competing opportunities and prioritize initiatives based on business impact, feasibility, and strategic alignment.
- Support multi-year roadmaps for AI, analytics, and decision-support capabilities.
- Measure outcomes and ensure solutions deliver meaningful business results.
Typical Education
Bachelor's or Master's degree in Data Science, Operations Research, Supply Chain, Business Analytics, Engineering, Economics, or a related field, or equivalent industry experience.
Relevant Experience
Required
- Bachelor's or Master's degree in Data Science, Operations Research, Supply Chain, Business Analytics, Engineering, Economics, or a related field, or equivalent industry experience.
- 5+ years of experience in commercial, supply chain, planning, procurement, operations, or related business functions.
- Experience working with analytics, machine learning, or AI solution teams.
- Proficiency with SQL and data analysis tools.
- Strong understanding of the machine learning lifecycle and AI solution delivery.
- Demonstrated success working with both technical and business stakeholders.
Preferred
- Experience in food, agriculture, or consumer products industries.
- Experience supporting sales, demand planning, supply planning, S&OP, procurement, pricing, forecasting, logistics, or customer service functions.
- Experience designing or delivering custom analytics applications that complement enterprise platforms such as SAP, Salesforce, or similar systems.
- Exposure to optimization, forecasting, recommendation systems, agentic AI, or decision intelligence solutions.
- Familiarity with enterprise data platforms and modern AI technologies.
Impact & Key Competencies
In this role, you will help shape how Simplot applies data science, machine learning, and AI to improve decision-making across commercial, planning, and supply chain processes. Working across Global Food, AgriBusiness, and corporate functions, you will identify opportunities, guide solution development, and help deliver practical capabilities that improve revenue growth, customer service, operational performance, and supply chain effectiveness.
- Business Partnership: Builds trusted relationships across diverse business functions and operating segments.
- Systems Thinking: Understands end-to-end commercial, planning, and supply chain processes and their interdependencies.
- Analytical Rigor: Uses data to evaluate opportunities, validate assumptions, and drive decisions.
- Strategic Prioritization: Focuses effort on initiatives with the greatest business impact.
- Translation Skills: Converts business needs into technical requirements and technical capabilities into business value. Job Requisition ID: 27563
Travel Required:
Location(s): Simplot Headquarters - Boise
Country: United States
**The Simplot Company is proud to be an Equal Opportunity Employer and will consider all qualified applicants for employment without regard to race, color, religion, national origin, ancestry, age, sex, gender, gender identity, gender expression, genetic information, physical or mental disability, medical condition, sexual orientation, military or veteran status, marital status, or any other protected status.
If the Simplot Company decides to offer you this position, such offer will be conditioned on your satisfactory completion of a post-offer criminal background check. For Washington Job Applicants, see the Washington State Attorney General’s Washington Fair Chance Act Guide and RCW 49.94.010 for more information.**
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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Simplot Company, 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 Required
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 $212,000 based on 11,866 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Simplot Company AI Hiring
Simplot Company has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Boise, ID, US.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 roles, $210,000 median).
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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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