Data Scientist - W2 Only

Job Overview:
Required:
• Master’s or PhD degree in Data Science, Computer Science, Statistics, Applied Mathematics, Machine Learning, or a related quantitative field.
• At least 3 years of relevant professional experience.
• Strong hands-on experience with Python programming.
• Experience writing modular, testable, maintainable, and production-grade code.
• Practical experience with data extraction, cleaning, transformation, and analysis.
• Strong understanding of statistical and machine learning techniques.
• Hands-on experience developing and productionizing machine learning pipelines.
Preferred:
• 8+ years of experience in a technical professional environment.
• Experience with Databricks and Spark-based machine learning pipelines.
• Experience deploying machine learning models to production environments.
• Experience with ML orchestration, monitoring, and optimization.
• Knowledge of SQL and experience working with large-scale datasets.
• Experience with ML lifecycle tools such as MLflow, CI/CD pipelines, and model-monitoring platforms.
• Experience with Azure or other cloud platforms.
• Experience with parallel processing and distributed computing.
• Experience creating dashboards or data visualizations using Tableau or Power BI.
• Airline or transportation industry experience.
Responsibilities:
• Develop, refactor, test, and optimize production-quality Python code for machine learning applications.
• Design, build, deploy, and maintain machine learning models and pipelines.
• Productionize ML workflows, including model deployment, orchestration, monitoring, optimization, and troubleshooting.
• Extract, clean, transform, and analyze large datasets to support business and customer intelligence initiatives.
• Apply statistical methods, machine learning techniques, and feature engineering to improve model performance.
• Work with data scientists, data engineers, product partners, and business stakeholders to deliver scalable ML solutions.
• Improve existing codebases, pipelines, and system integrations for reliability, maintainability, and performance.
• Debug technical issues and conduct performance tuning across the ML lifecycle.
• Use cloud platforms and parallel-processing technologies to scale model development and deployment.
• Document technical solutions and communicate findings clearly to both technical and non-technical audiences.
• Support quality, innovation, scalability, and measurable business or revenue impact across the organization.