Data Scientist

Job description:

Master's/PhD degree with 3+ years of experience in quantitative discipline (e.g., Data Science, Machine Learning Engineer, Computer Science, Applied Mathematics, Statistics, etc.)
Experience with Python programming language
Practical experience with data extraction, cleaning, and analysis
Depth of knowledge in statistical and machine learning techniques

Preferred qualifications Education & Prior Job Experience
8+ years of experience in a technical professional environment, in addition to the minimum requirements
Experience with SQL and data visualization (Tableau, PowerBI)
Practical experience designing, building and deploying machine learning models
Experience in using cloud platforms and parallel processing to scale model development/ deployment (Databricks, Azure)
Domain knowledge in the airline industry
Experience working in a consulting role

Skills, Licenses & Certifications
Ability to effectively communicate both verbally and written with all levels within the organization
Demonstrated motivation and aptitude for logical analysis, problem identification, and problem solving
Ability to view data from different angles to employ feature engineering techniques to better represent models
Ability to work on a diverse team with diverse skillsets

Top 3 Must Have Skills: MS/PhD degree, Python, Machine Learning, Deep Learning

Nice to Have Skills: Databricks, Azure ML, SQL, Tableau/PowerBI

Describe a great candidate that you are looking for and what skills and experience they will have:

Experienced data scientist with ML/DL model training, deployment, and evaluation. Advanced in Python and Databricks. Good communication and collaboration. Consulting background is a plus.

What is the team environment and structure like?: Centralized ML team supporting 7 business units. All team members are data scientist levels from senior to principal level. Team culture is friendly, supportive, and collaborative. Focus on quality, innovation, and revenue impact.

How will the resource(s) fit into your team?: The resource will embed within the centralized ML team and support active Loyalty and Marketplace projects. They will independently execute model development, validation, and deployment tasks while collaborating with peer data scientists, data engineers, and product partners