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About This Role
APM Terminals Step into the engine room of global commerce. At APM Terminals Elizabeth, you’ll be at the center of one of the busiest port complexes in North America. Every container you touch keeps freight moving through the Port of New York / New Jersey, supporting critical supply chains across the East Coast and beyond.
This is a place where speed, scale, and precision come together. You’ll collaborate with high- performing teams in a fast-paced, high-volume environment where safety and operational excellence are the top priorities. Here, your impact is immediate—and global.
Why you’ll love it here
- Be part of a major gateway on the U.S. East Coast
- Experience high-volume, complex terminal operations
- Work with driven teams in a fast-moving environment
- Continuous improvement mindset – where curiosity is encouraged, root-cause problem solving is valued, and making things better every day is part of how we win
Data Science and Analytics Manager
Based at APM Terminals Elizabeth, the Data Science and Analytics Manager will shape and lead the terminal’s analytics capability, turning complex operational data into decisions that improve safety, service, productivity, capacity, and cost performance. This role offers end-to-end ownership of the analytics roadmap, from identifying high-value opportunities through developing, deploying, and scaling solutions used in live terminal operations.
What We Can Offer
At APM Terminals, we foster a dynamic learning and training culture that empowers our employees to excel. Our commitment to improvement means every team member has the opportunity to develop their skills and advance their careers by continuously refining how we work. Experience a diverse, multinational workplace where your operational insights directly influence how we work.
- Lead and define a high-impact analytics agenda that directly supports terminal performance and modernization initiatives.
- Work with rich operational data and influence daily decisions across vessel, yard, gate, rail, labor, equipment, and asset management activities.
- Gain exposure to operational and senior leadership teams while collaborating with regional and global analytics communities.
- Build and develop an emerging analytics capability while helping drive technology and operational transformation.
Key Responsibilities
- Define and execute the terminal’s data science and analytics strategy, roadmap, operating model, and prioritized portfolio aligned with business priorities.
- Partner with operational and functional leaders to identify high-value business problems, establish baselines, and deliver measurable outcomes.
- Develop predictive, prescriptive, and optimization solutions across operational use cases including productivity, capacity, forecasting, reliability, and planning.
- Lead analytics products through the full lifecycle from problem definition and data preparation to deployment, monitoring, and continuous improvement.
- Establish trusted KPI frameworks, reporting solutions, reusable data products, and self-service analytics capabilities.
- Translate complex analytical findings into actionable recommendations, scenarios, and decision-support tools for operational and senior leaders.
- Promote data quality, governance, responsible AI practices, and alignment with enterprise technology standards while building analytics capability across the organization.
What We Are Looking For
- Continuous improvement mindset: we are looking for someone who brings a thoughtful improvement mindset—curious about how work gets done and motivated to make meaningful, sustainable improvements over time.
- Leadership expectation: We are looking for people who consistently put customers first, lead by example with respect for people, deliver results with integrity, and drive continuous improvement by solving problems at the root, developing people, and raising standards every day.
- Master’s degree in data science, computer science, statistics, mathematics, engineering, operations research, or a related quantitative discipline, or equivalent practical experience.
- Proven success delivering analytics, machine learning, optimization, or decision-support solutions in complex operational, industrial, logistics, transportation, supply chain, or asset-intensive environments.
- Strong Python and SQL expertise, including experience working with common data science libraries and modern data environments.
- Experience taking analytical solutions from concept through production deployment, adoption, and performance monitoring while delivering measurable operational or financial improvements.
- Ability to communicate complex analysis clearly, influence stakeholders, and lead people, projects, vendors, and cross-functional teams.
- Experience with cloud platforms, business intelligence tools, data engineering practices, MLOps, and advanced analytics techniques such as forecasting, optimization, simulation, anomaly detection, computer vision, generative AI, large language models, or digital twins is advantageous.
- Ability to obtain and maintain a Transportation Worker Identification Credential (TWIC) and Port Access Card.
Salary: $130K - $150K
Location: Elizabeth, NJ (5 days in office)
Notice to applicants applying to positions in the United States
Applicants must be authorized to work for any employer in the U.S.
APM Terminals is committed to equal employment opportunity and providing reasonable accommodations to applicants with physical and/or mental disabilities who are applying for positions in the U.S. We value and encourage diversity and solicit applications from all qualified applicants without regard to race, color, gender, sex, age, religion, creed, national origin, ancestry, citizenship, marital status, sexual orientation, physical or mental disability, medical condition, veteran status, gender identity, genetic information, or any other characteristic protected by federal, state, or local law.
If you are interested in applying for employment with APM Terminals in the U.S. and need special assistance or an accommodation to use our website or to apply for a position, or if you need a reasonable accommodation to perform a job, please contact the applicable Human Resources Department by emailing [email protected] . Determination on requests for reasonable accommodation are made on a case-by-case basis pursuant to an interactive dialogue between the applicant and the Company.
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Maersk is committed to a diverse and inclusive workplace, and we embrace different styles of thinking. Maersk is an equal opportunities employer and welcomes applicants without regard to race, colour, gender, sex, age, religion, creed, national origin, ancestry, citizenship, marital status, sexual orientation, physical or mental disability, medical condition, pregnancy or parental leave, veteran status, gender identity, genetic information, or any other characteristic protected by applicable law. We will consider qualified applicants with criminal histories in a manner consistent with all legal requirements.
We are happy to support your need for any adjustments during the application and hiring process. If you need special assistance or an accommodation to use our website, apply for a position, or to perform a job, please contact us by emailing [email protected] .
Salary Context
This $130K-$150K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At APM Terminals, 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 $212,000 based on 11,866 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($140K) sits 34% below the category median. Disclosed range: $130K to $150K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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: $272,250; VP: $235,000.
APM Terminals AI Hiring
APM Terminals has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Elizabeth, NJ, US. Compensation range: $150K - $150K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 roles, $210,000 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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).
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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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