Interested in this AI/ML Engineer role at IWCO?
Apply Now →Skills & Technologies
About This Role
Overall Summary:
The Data Science Operations Analyst plays a key role in supporting data\-driven direct marketing campaigns that deliver highly targeted, performance driven results. This position is responsible for supporting the data foundation behind campaign execution and advanced analytics, focusing on hands\-on execution of recurring data processes that support campaign delivery at scale. Working closely with cross\-functional teams, the Data Science Operations Analyst support audience segmentation, data processing, marketing experiments, and analytics initiatives while identifying opportunities to improve efficiency through automation and AI\-enabled tools.
Primary Duties/Responsibilities:
- Execute data preparation and campaign operations, including audience segmentation, list generation, file processing and data appends
- Support test design execution and development of measurement datasets for marketing experiments
- Leverage AI tools (e.g. Copilot, ChatGPT, Claude) to streamline workflows, automate repetitive tasks, and improve speed and accuracy
- Perform data manipulation, cleansing and quality validation to ensure readiness for marketing campaign execution
- Identify opportunities to enhance processes through automation and improved data practices
- Collaborate cross\-functionally with Data Science, Marketing and Operations teams to deliver high\-quality client outcomes
- Adhere to IWCO standards for data security, governance, and operational excellence
- Perform other (or other related) duties as applicable or assigned.
Required Skills/Abilities/Competencies:
- Demonstrated SQL and Python programming skills
- Interest in marketing analytics, customer data, and applied data science
- Ability to present analytical findings to a non\-technical audience through reports, dashboards and presentations
- Ability to translate complex datasets into clear actionable insights by using data visualization and story\-telling techniques
- Ability to manage multiple priorities in a fast\-paced, production\-oriented environment
- Curiosity and a mindset of continuous improvement
- Strong communication skills and ability to build relationships
- Understanding of Agile Project Management concepts is preferred
- Proficiency in Microsoft Office, especially Teams, Word, PowerPoint, and Excel
Education and Experience:
- Bachelor’s degree in Data Science, Marketing Analytics, Statistics, Computer Science. or a related field (or equivalent experience)
- Experience with Business Intelligence tools such as Tableau or Power BI
- Familiarity with AI tools and applications in data workflows
- Experience with modern cloud platforms such as Snowflake and Databricks is preferred
- Experience applying Data Science to real business problems is preferred
Physical Requirements:
- Ability to work 8 hours consecutively.
- Prolonged periods of sitting at a desk and working on a computer.
Salary:
The starting salary range for this position is $90,000\-95,000\.
At IWCO, base pay is determined by job\-related knowledge, skills, credentials, and experience, along with factors such as role scope and location. Candidates seeking compensation outside of the posted range are encouraged to apply and will be considered based on their individual qualifications and/or may be considered for other positions.
Pay is influenced by a variety of factors specific to the position, including market conditions and, in some cases, education, work experience, and certifications. Beyond competitive pay, IWCO is committed to supporting our team members and their families with comprehensive benefits. These may include health, dental, and vision insurance, life insurance, and other wellness programs. Eligible employees also enjoy 401(k) plans, paid holidays, vacation time, sick leave, and more. At IWCO, we invest in you so you can take care of what matters most.
IWCO is an Equal Opportunity Employer. We welcome diversity and provide equal employment opportunities without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability status, veteran status, or any protected status as defined by law. Accommodations are available for individuals with disabilities upon request. Contact our HR Department for more information.
Equal Opportunity Employer/Protected Veterans/Individuals with Disabilities
This employer is required to notify all applicants of their rights pursuant to federal employment laws. For further information, please review the Know Your Rights (https://www.eeoc.gov/poster) notice from the Department of Labor.
Salary Context
This $90K-$95K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1841 roles with salary data).
View full AI/ML Engineer salary data →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 IWCO, 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($92K) sits 58% below the category median. Disclosed range: $90K to $95K.
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.
IWCO AI Hiring
IWCO has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Chanhassen, MN, US. Compensation range: $95K - $95K.
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 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.