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About This Role
Company Description Work with Us. Change the World.
At AECOM, we're delivering a better world. Whether improving your commute, keeping the lights on, providing access to clean water, or transforming skylines, our work helps people and communities thrive. We are the world's trusted infrastructure consulting firm, partnering with clients to solve the world’s most complex challenges and build legacies for future generations.
There has never been a better time to be at AECOM. With accelerating infrastructure investment worldwide, our services are in great demand. We invite you to bring your bold ideas and big dreams and become part of a global team of over 50,000 planners, designers, engineers, scientists, digital innovators, program and construction managers and other professionals delivering projects that create a positive and tangible impact around the world.
We're one global team driven by our common purpose to deliver a better world. Join us.
Job Description AECOM is hosting an exclusive, invitation-only hiring event in Arlington on November 18th and 19th. This is a great opportunity to learn and connect with AECOM for career opportunities starting Spring/Summer 2027.
The event kicks off on Wednesday evening with a networking mixer from 6-8 pm, where you can hear from AECOM staff about various projects, career paths, and the company itself. You’ll also have the chance to meet other students, recent graduates, and AECOM professionals from across our business lines.
On Thursday, interviews will take place at our Arlington office. This is a fantastic opportunity to learn about and connect with AECOM for early career opportunities.
AECOM is actively seeking a highly talented, energetic technology-minded Entry-Level Data Scientist. The selected candidate will report to the Data Science Manager in the Technology Services Group and support both internal and external clients within the Environment Business line. This position will support a wide variety of AECOM projects including environmental permitting, natural disaster recovery, environmental restoration, construction, site assessments, inspections, energy infrastructure development, water resources, transportation, and more. A successful candidate will work both independently and in close coordination with various AECOM staff and project teams to develop, deploy and support GIS and other Geospatial Data Science tasks critical to successful project delivery.
This position is expected to start Spring/Summer 2027.
Responsibilities include, but are not limited to:
- Analyze large datasets to identify patterns, derive insights, and produce meaningful visualizations
- Assist with automation of data processing tasks
- Conduct statistical analysis and modeling for various environmental use-cases
- Apply attention to detail with proactive and resourceful problem-solving skills.
Qualifications Minimum Requirements
- Bachelor's Degree in Geospatial Sciences, Computer Science, Geography, Social Science, Environmental Science, Data Science, Engineering, or related field.
- Due to the nature of the work, U.S. Citizenship is required for this role.
Preferred Qualifications
- Familiarity with programming languages such as Python and R for data cleaning, statistical analysis, and data visualization
- Experience with data visualization tools such as PowerBI and Tableau
- Strong analytical skills and problem-solving ability
- Comfortable working outside and/or traveling occasionally to support field activities.
- Familiarity with ArcGIS Pro and GIS data is prefered
- Comfortable working in a fast-paced multi-tasking environment.
Additional Information
- Relocation assistance is not available for this position.
At AECOM, we are committed to maintaining a secure and trustworthy recruitment process and take any fraudulent hiring activity seriously. To support this commitment, all newly hired employees are required to attend an in-person Day 1 onboarding at an AECOM office location as a condition of employment.
About AECOM
AECOM is proud to offer comprehensive benefits to meet the diverse needs of our employees. Depending on your employment status, AECOM benefits may include medical, dental, vision, life, AD&D, disability benefits, paid time off, leaves of absences, voluntary benefits, perks, flexible work options, well-being resources, employee assistance program, business travel insurance, service recognition awards, retirement savings plan, and employee stock purchase plan.
AECOM is the global infrastructure leader, committed to delivering a better world. As a trusted professional services firm powered by deep technical abilities, we solve our clients’ complex challenges in water, environment, energy, transportation and buildings. Our teams partner with public- and private-sector clients to create innovative, sustainable and resilient solutions throughout the project lifecycle – from advisory, planning, design and engineering to program and construction management. AECOM is a Fortune 500 firm that had revenue of $16.1 billion in fiscal year 2025. Learn more at aecom.com.
What makes AECOM a great place to work
You will be part of a global team that champions your growth and career ambitions. Work on groundbreaking projects - both in your local community and on a global scale - that are transforming our industry and shaping the future. With cutting-edge technology and a network of experts, you’ll have the resources to make a real impact. Our award-winning training and development programs are designed to expand your technical expertise and leadership skills, helping you build the career you’ve always envisioned. Here, you’ll find a welcoming workplace built on respect, collaboration and community—where you have the freedom to grow in a world of opportunity.
As an Equal Opportunity Employer, we believe in your potential and are here to help you achieve it. All your information will be kept confidential according to EEO guidelines.
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 9,653 AI roles we're tracking, Data Scientist positions make up 5% of the market. At AECOM, 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 1,406 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $110,408.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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: $272,275; VP: $235,000.
AECOM AI Hiring
AECOM has 1 open AI role right now. They're hiring across Data Scientist. Based in Arlington, VA, US.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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).
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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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