Data Science & Analytics Manager

APM Terminals•Elizabeth, NJ

1d•$130,000 - $150,000•Onsite

About The Position

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. 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.

Requirements

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.
Ability to obtain and maintain a Transportation Worker Identification Credential (TWIC) and Port Access Card.

Nice To Haves

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.

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.