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.