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About This Role
Location:
Kansas City, MO, US, 64106
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Best People \+ Right Culture. These are the driving forces behind JE Dunn’s success.
By hiring inspired people, giving them interesting and challenging work, enabling them with innovative tools, and letting them share in the company’s rewards, we’ve found a sustainable way to grow in our industry for the last 100\+ years.
Our diverse teams around the country strive to enrich lives through inspired people and places everyday, and we need inspired people like you to join us in building authentic partnerships.
Role Summary
The Data Analyst 2 will collect, compile, transform, and analyze data, metrics, and statistics from various internal and external sources to create reports and insights to inform management. This role will also act as a specialist on reporting tools and dashboards within the area of specialization. All activities will be performed in support of the strategy, vision and values of JE Dunn.
Autonomy \& Decision\-Making: Makes decisions on assigned areas of responsibility, provides recommendations to supervisor and refers all exceptions to supervisor, as needed.
Career Path: Senior Data Analyst
Key Role Responsibilities \- Core
DATA ANALYST FAMILY \- CORE
Gathers and analyzes data from a variety of internal and external sources dependent on function supported (e.g. HRIS, CRM, Databases etc.)
Maintains knowledge of key operational metrics within the assigned function and other related teams
Extracts large data sets from various sources to facilitate reporting and analysis, manipulating and cleaning data as needed
Utilizes data visualization tools such as Power BI or Tableau to effectively communicate metrics and insights to management
Audits data, data sources and processes ensuring compliance with data privacy regulations and best practices
Supports leadership with both scheduled and ad\-hoc reporting requests
May assist with developing or revising data analytics strategy within the defined function to support the broad organizational data strategy
Builds tools and processes to improve data management and reporting efficiency under direction from leadership
Documents historical reporting, parameters and datasets to allow time\-series comparisons
Key Role Responsibilities \- Additional Core
DATA ANALYST 2
In addition, this role will be responsible for the following:
Partners with business leaders to understand operational needs and build timely and scalable reports using various data sources
Advises team members on data resources and tools available, assisting with finding, cleaning, and analyzing raw data for use
Identifies relevant data sets for area of specialization building reports and dashboards applicable to both strategic decision making and day\-to\-day operations
Partners with internal data consulting team on large scale or complex business data requests
Advises on best practices for integrating technology and tools into daily practices across assigned function
Builds custom reports and templates for use by team members as needed
Uses knowledge of reporting to make recommendations on data applicable to the operations of assigned functions
Partners with data analytics professionals across the organization to enhance data quality in various systems
Knowledge, Skills \& Abilities
Ability to perform work accurately and completely, and in a timely manner
Communication skills, verbal and written
Proficiency in MS Office – Intermediate
Proficiency in MS Excel – Intermediate
Proficiency in Power Apps and Power Automate
Experience in Power BI\- Intermediate
Knowledge of SQL, including creating simple SQL queries
Knowledge of integrations and API development
Ability to connect to multiple data sets, import data, and transform the data to support data visualizations and further analyses
Proficiency in delivering data visualizations to internal customers based on project requirements
Ability to compile statistics and prepare statistical reports
Ability to prioritize multiple projects
Attention to detail and the ability to evaluate data against expected results
Ability to think analytically for translating data into informative visuals and reports
Ability to communicate to various audiences
Ability to build relationships and collaborate within a team, internally and externally
Ability to influence change through innovation and process improvement
Time Management Skills
Listening Skills
Education
Bachelor’s degree in data science, analytics or related field (Required)
In lieu of the above requirements, relevant experience will be considered
Experience
2\+ years’ experience in data analysis (Preferred)
2\+ years’ experience in visualization tools (Preferred)
Working Environment
Must be able to lift up to 10 pounds
Typically travel is not required
Normal office environment
Frequent activity: Sitting, Viewing Computer Screen
Occasional activity: Standing, Walking, Reaching above Shoulder
Benefits Information
The benefits package aligned to this position is Professional Non\-Union.
This role is expected to accept applications for at least three business days and may continue to be posted until a qualified applicant is selected or the position has been cancelled.
JE Dunn Construction is committed to hiring and retaining a diverse workforce. We are proud to be an Equal Opportunity/Affirmative Action Employer and it is our policy to provide equal opportunity to all people without regard to race, color, religion, national origin, ancestry, marital status, veteran status, age, disability, pregnancy, genetic information, citizenship status, sex, sexual orientation, gender identity or any other legally protected category. JE Dunn Construction is a background screening, drug\-free workplace.
JE Dunn provides reasonable accommodations to qualified individuals with disabilities. If you would like to request a reasonable accommodation in order to apply for a job, please submit your request to [email protected]
JE Dunn Construction Company does not accept unsolicited resumes from search firms or agencies. Any resume submitted to any employee of JE Dunn Construction without a prior written search agreement will be considered unsolicited and the property of JE Dunn Construction Company. Please, no phone calls or emails.
\*\*\*For this opportunity, JE Dunn Construction will not sponsor an applicant for work visa status or employment authorization, nor will we offer any immigration\-related support for this position (including but not limited to H\-1B, F\-1 OPT, F\-1 STEM OPT, F\-1 CPT, J\-1, TN\-1 or TN\-2, E\-3, O\-1, or future sponsorship for U.S. lawful permanent residence status.)\*\*\*
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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At JE Dunn Construction, 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
JE Dunn Construction AI Hiring
JE Dunn Construction has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Kansas City, MO, US.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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