Staff Data Scientist: Aviation Ops & Optimization

Atlas Air; Erlanger (KY); On-site; USD 165,000 - 225,000; Full time

Benefits offered by this job

Medical, dental and vision insurance

Generous paid time off

401K contributions

Job summary

Atlas Air Worldwide is seeking a Staff Data Scientist to lead data-driven solutions for high-dimensional aviation optimization problems. You will build predictive and prescriptive models that inform operations, collaborate with cross-functional teams, and translate complex concepts for senior leaders.

The role requires a strong programming backbone, ML expertise, and operations research know-how, with mentoring responsibilities and a drive to push for impactful insights across the enterprise.

Qualifications

• Master's degree or Ph.D. in a quantitative field (OR, ML, CS, DS, statistics, math).
• 5+ years in data science or analytics with demonstrated impact.
• Proficiency in Python, R or similar for data analysis.
• Strong knowledge of statistics, modeling and optimization.
• Ability to translate business needs into technical solutions.

Responsibilities

• Lead complex data science projects from conception to implementation with stakeholders.
• Design and maintain predictive and prescriptive models to solve aviation problems.
• Develop production-grade Python code with git, tests and best practices.
• Create clear visualizations and narratives for technical and non-technical audiences.
• Mentor junior data scientists and stay current with emerging techniques.

Skills

Python

SQL

Pandas

Spark

Machine learning

Git

Education

Master's degree or Ph.D.

Tools

scikit-learn

PyTorch

TensorFlow

Gurobi

CPLEX

OR-Tools

Job description

Atlas Air Worldwide is seeking a Staff Data Scientist to lead data-driven solutions for high-dimensional aviation optimization problems. You will build predictive and prescriptive models that inform operations, collaborate with cross-functional teams, and translate complex concepts for senior leaders.

The role requires a strong programming backbone, ML expertise, and operations research know-how, with mentoring responsibilities and a drive to push for impactful insights across the enterprise.