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About This Role
Financial Planning \& Data Analytics AI Manager
\- (194583\)
At HDR, our employee\-owners are fully engaged in creating a welcoming environment where each of us is valued and respected, a place where everyone is empowered to bring their authentic selves and novel ideas to work every day. As we foster a culture of inclusion throughout our company and within our communities, we constantly ask ourselves: What is our impact on the world?
Watch Our Story:' https://www.hdrinc.com/our\-story'
Each and every role throughout our organization makes a difference in our ability to change the world for the better. Read further to learn how you could help make great things possible not only in your community, but around the world.
This individual will lead the evolution of financial planning and analysis through advanced analytics, machine learning, artificial intelligence, and data\-driven forecasting. This role will maintain ownership of core FP\&A reporting, forecasting, and analytics while driving the development of predictive models, automated insights, and AI\-enabled decision support tools. The successful candidate will partner closely with Finance, Marketing, and Data \& Analytics teams to transform enterprise data into forward\-looking business intelligence that improves forecasting accuracy, identifies emerging trends, and supports strategic decision making.
In the role of Financial Planning \& Data Analytics AI Manager, we'll count on you to:* Own reporting for key financial metrics, ensuring data accuracy, consistency, and integrity.
- Design and deploy predictive forecasting models utilizing machine learning techniques.
- Build forecasting solutions leveraging internal and external data sources.
- Evaluate and implement AI tools that improve financial planning and business decision\-making.
- Champion innovation and continuous improvement in Finance processes.
- Stay current on emerging AI and analytics technologies and recommend practical business applications.
- Lead development and enhancement of Power BI dashboards and enterprise analytics solutions.
- Effectively communicate complex analytical findings to executive and non\-technical audiences.
- Collaborate with cross\-functional teams across Finance, Operations, Marketing, and Data \& Analytics
- Special projects as assigned by supervisor, Director of Financial Planning and Analysis, Chief Financial Officer and/or others
Preferred Qualifications
- Experience developing predictive models or machine learning solutions.
- Knowledge of Python, SQL, Snowflake, or similar analytics platforms.
- Strong analytical and problem\-solving skills.
- Experience working with large, complex datasets.
- Excellent communication and presentation skills.
- Experience applying AI, machine learning, or advanced statistical techniques in a business environment.
- Experience with generative AI technologies, Microsoft Copilot, or similar platforms.
- Advanced proficiency in Power BI, Excel, and data visualization.
- Experience with financial forecasting, budgeting, and business performance analysis.
Required Qualifications
- Bachelor's degree in accounting, finance, business analytics, mathematics, economics or applicable field.
- A minimum of 8 years of experience in a large organization serving in a finance or accounting role.
- Excellent written and verbal communication skills are critical as this role will present analysis and interact directly with Senior Executives and all levels of management.
- Must be able to present financial information to finance and non\-financial users in a succinct and easily understandable manner.
- This individual must excel at "telling the story" of the financials and key performance indicators.
- Strong leadership skills and ability to bring a cohesive approach to processes in a matrix organizational structure.
- Must demonstrate critical thinking and problem\-solving skills. Willing to dig into the details of the data.
- Ideal candidate is highly organized and detailed oriented, questions the status quo and is always seeking process improvement and efficiencies in reporting to deliver timely, quality and accurate management reporting.
- Expert in Microsoft Excel and PowerPoint.
What We Believe
HDR is our company. Together, we build on each other's life experiences and perspectives to make great things possible every day. This shapes our collaborative culture, encourages organizational trust and connects us closer to the clients and communities we serve.
Our Commitment
As employee owners, we all have a role in creating an inclusive environment where each of us is welcomed, valued, respected and empowered to bring our authentic selves to work every day.
Our eight Employee Network Groups (Asian Pacific, Black, Hispanic/Latino(a), LGBTQ\+, People with Disabilities, Veterans, Women, Young Professionals) help create a sense of belonging and foster a supportive environment where everyone is empowered to engage and contribute. Each group has an executive sponsor and is open to all employees.
Primary Location:
United States\-Nebraska\-Omaha
Industry:
Corporate/Support Services
Schedule:
Full\-time
Employee Status:
Regular
BusinessClass:
Marketing and Admin
Job Posting:
Jul 27, 2026
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 HDR, 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.
HDR AI Hiring
HDR has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Omaha, NE, 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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