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About This Role
Applied Data Scientist - AI Enablement (Transportation) - (193485) 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.
We believe transportation is more than movement, it’s the foundation of connected, thriving communities. As part of HDR’s Transportation Business Group, you’ll help shape the systems that move people and goods safely, efficiently, and sustainably. From designing resilient highways and iconic bridges to advancing transit, passenger and freight rail, aviation, federal transportation ports and marine infrastructure, your work will directly support economic vitality, public safety, sustainable and resilient communities and quality of life. We bring together planners, engineers, architects, construction management staff, environmental, strategic communications, economists, management consultants and specialists across disciplines to solve complex mobility challenges with innovation, technical excellence, and a deep understanding of community needs. Whether you're modernizing aging infrastructure or pioneering next-generation transportation solutions, your contributions will help define the future of mobility. This isn’t just a job, it’s a chance to lead progress, drive meaningful impact, and leave a legacy of smarter, more connected transportation networks. We are all employee-owners at HDR, which is the foundation of our collaborative culture that connects employees around the world.
Primary Role:
The Applied Data Scientist – AI Enablement is a high-impact individual contributor role focused on building internal AI capabilities and removing systemic constraints that limit HDR’s ability to scale artificial intelligence across the Transportation Business Group. In the initial phase, this role is primarily internally focused, developing AI solutions, improving data and technology foundations, and identifying bottlenecks in systems, processes, and tooling. As internal capabilities mature, the role will progressively transition to client-facing AI delivery and advisory work, leveraging internally proven solutions as repeatable services.
Utilization Expectations:
- 40% Internal AI solution development
- 40% Client AI delivery and pilot engagements (as capabilities mature) (0% year 1)
- 20% Skill development and continuous learning
Primary Responsibilities:
Internal AI Capability Development:
- Design, develop, and deploy AI solutions addressing internal transportation business needs (e.g., document intelligence, predictive analytics, copilots, workflow automation).
- Build reusable AI components, data pipelines, and workflows that can be scaled for future client delivery
- Manage full model lifecycle including development, deployment, monitoring, drift detection, retraining, and inference.
Constraint & Bottleneck Identification:
- Proactively identify limitations in data availability, system integration, tooling, governance, and processes that inhibit AI adoption.
- Partner with IT, data architects, and business stakeholders to recommend scalable improvements.
- Document technical debt and architectural gaps that must be addressed before scaling AI capacity.
Collaboration & Communication:
- Collaborate with cross-functional stakeholders to translate ambiguous problems into structured AI solutions.
- Communicate AI concepts clearly to both technical and non-technical audiences.
Future Client-Facing Work:
- Support client pursuits and pilot projects using internally developed AI capabilities.
- Contribute to the evolution of repeatable AI services and offerings.
Note: This is not an exhaustive list. This role requires flexibility to perform other duties as required.
Preferred Qualifications:
- Bachelor’s degree in a closely related field or equivalent combination of education and experience.
- Experience with generative AI and modern AI services.
- Hands-on experience developing, deploying, and maintaining data and AI solutions in cloud environments (e.g., Azure, AWS, or GCP), including cloud-native data pipelines, scalable compute, and production model deployment.
- Experience managing end-to-end data science projects and model lifecycles.
- Strong analytical, organizational, and communication skills.
- Advanced Degree
- Experience designing or improving data pipelines and AI infrastructure.
- Consulting or professional services experience.
- Six Sigma or other process improvement certifications.
Required Qualifications* A degree in a closely related field or combination of education and relevant experience
- A minimum of 3 years experience with data engineering tools and languages such as SQL, Power Query, and Pandas
- A minimum of 3 years experience with business intelligence tools such as Power BI, Tableau, Plotly, Seaborn, and Matplotlib
- A minimum of 3 years experience with data science languages such as Python and R
- Self-motivated, detail-oriented professional, ability to multitask a must
- Proficiency with MS Office including Word and Outlook
- Ability to handle confidential information
- Excellent writing and people skills
- Strong math and organizational skills
- Flexibility and ability to prioritize and handle multiple tasks and various managers in a fast-paced environment
- Excellent verbal and written communication skills including grammar, punctuation, proofreading, spelling and telephone skills
- An attitude and commitment to being an active participant of our employee-owned culture is a must
- In-depth knowledge of machine learning algorithms, statistical models, and data analytics
- Experience completing multiple data science projects
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.
We provide a comprehensive benefits package that promotes employee ownership, employee health, performance, and success, which includes medical, dental, vision, short and long-term disability, life insurance, an employee assistance program, paid time away, parental leave, paid holidays, a retirement savings plan with employer match, employee referral bonus and tuition reimbursement. The expected compensation range for this position depends upon skills, experience, education and geographical location. (Stated benefits are for full-time regular positions. Temporary and part-time roles eligible for limited benefits.) US Locations: $150,000 - $215,000.Primary Location
: United States-Nebraska-OmahaOther Locations
: United States-Texas-Round Rock, United States-North Carolina-Charlotte, United States-Florida-Doral, United States-Wyoming-Cheyenne, United States-California-Long Beach, United States-Minnesota-Saint Louis Park, United States-Florida-Fort Lauderdale, United States-West Virginia-Weirton, United States-Oregon-Salem, United States-Maryland-Baltimore, United States-Texas-Fort Worth, United States-Montana-Missoula, United States-District of Columbia-Washington DC, United States-Texas-Houston, United States-California-Irvine, United States-Wisconsin-Madison, United States-Florida-Tallahassee, United States-Mississippi-Jackson area, United States-Nevada-Reno, United States-Iowa-Des Moines, United States-Oregon-Portland, United States-Montana-Bozeman, United States-Texas-El Paso, United States-Texas-Corpus Christi, United States-Washington-Seattle, United States-Nevada-Las Vegas, United States-Montana-Billings, United States-Texas-Dallas, United States-Montana-Helena, United States-Washington-Spokane, United States-Iowa-Cedar Rapids, United States-Michigan-Grand Rapids, United States-Texas-Austin, United States-Louisiana-Metairie, United States-Virginia-Virginia Beach, United States-Massachusetts-Boston, United States-Colorado-Denver, United States-Iowa-Ames, United States-Oregon-Bend, United States-Missouri-St Louis, United States-Arizona-Tucson, United States-Washington-Vancouver, United States-Colorado-Colorado Springs, United States-Louisiana-Lafayette, United States-Hawaii-Honolulu, United States-Oklahoma-Tulsa, United States-Arizona-Phoenix, United States-Kentucky-Louisville, United States-Georgia-Atlanta, United States-Illinois-Rosemont, United States-California-Walnut Creek, United States-Tennessee-Memphis, United States-Montana-Kalispell, United States-Louisiana-Baton Rouge, United States-Virginia-Richmond, United States-Tennessee-Brentwood, United States-Ohio-Cleveland, United States-Florida-Tampa, United States-Ohio-Columbus, United States-Kentucky-Lexington, United States-Ohio-Cincinnati, United States-Washington-Bellevue, United States-Missouri-Kansas City, United States-South Dakota-Sioux Falls, United States-Alabama-Mobile, United States-Tennessee-Nashville, United States-New Jersey-Newark, United States-Kentucky-Paducah, United States-New Mexico-Albuquerque, United States-New York-White Plains, United States-South Carolina-Charleston, United States-South Carolina-Columbia, United States-Virginia-Vienna, United States-Florida-Orlando, United States-Alaska-Anchorage, United States-Minnesota-Minneapolis, United States-Indiana-Indianapolis, United States-Pennsylvania-Pittsburgh, United States-Wyoming-Lander, United States-California-San Diego, United States-Michigan-Lansing, United States-Pennsylvania-Philadelphia, United States-Nebraska-Lincoln, United States-Montana-Butte, United States-New Jersey-Pennington, United States-California-Sacramento, United States-Connecticut-Rocky Hill, United States-New York-New York, United States-Michigan-Ann Arbor, United States-Utah-Salt Lake City, United States-North Carolina-Winston-Salem, United States-Michigan-Detroit, United States-North Dakota-Bismarck, United States-Maine-Portland, United States-North Dakota-Fargo, United States-Illinois-Chicago, United States-California-Los Angeles, United States-Wyoming-Gillette, United States-Idaho-Boise, United States-North Carolina-Raleigh, United States-Texas-San AntonioIndustry
: Highway + RoadSchedule
: Full-timeEmployee Status
: Regular BusinessClass: Data and AutomationJob Posting
: Oct 16, 2026
Salary Context
This $150K-$215K range is above the median for Data Scientist roles in our dataset (median: $162K across 195 roles with salary data).
View full Data Scientist salary data →Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 11,520 AI roles we're tracking, Data Scientist positions make up 4% of the market. At HDR, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $190,000 based on 1,564 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. Disclosed range: $150K to $215K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
HDR AI Hiring
HDR has 2 open AI roles right now. They're hiring across Data Scientist. Positions span Omaha, NE, US, Washington, DC, US. Compensation range: $142K - $215K.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 roles, $210,000 median).
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 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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