Lead Data Scientist

Senior Data Science Lead - Travel AnalyticsLocation: Hybrid Role (Commutable to Atlanta, GA)

Architect cutting-edge Machine Learning solutions.Champion commercial driven Data Science.Nurture a culture of innovation within your team.Mentor emerging talent within the Data Science organization.Produce scalable machine learning algorithms.

• Leadership & Mentorship: Lead and nurture junior data analysts and datascientists, fostering their professional growth and integration into thevibrant culture of our Data Science organization.
• Technical Guidance & Quality Assurance: Act as a subject matter expert inmachine learning, overseeing the technical direction of projects andensuring the delivery of high-quality solutions through code reviews andthe adoption of agile methodologies.
• Innovation & Implementation: Design, develop, and deploy sophisticatedmachine learning algorithms for both batch and real-time data processing,ensuring they are scalable, efficient, and aligned with business goals.
• Algorithm Validation & Performance Monitoring: Lead rigorous statistical analysis to validate machine learning models, collaborate with cross-functional teams to refine algorithms based on empirical data, and establish metrics to monitor their performance continuously.
• Data Mastery & Collaboration: Enhance the team's understanding of travel-related data, including insights from Global Distribution Systems (GDS)and New Distribution Channels (NDC). Work closely with cross-functionalteams to leverage data from various sources for ML development.

Requirements/Qualifications:Educational Background: Must possess a Master’s degree in Data Science, Computer Science, or a related field.

Professional Experience:• Analyzing travel distribution data and crafting complex machine learning algorithms.• Applying agile development practices within the travel distribution context.• Utilizing Python, Big Data technologies, and other data science tools.• Implementing machine learning models and conducting large-scale data analysis with technologies such as Spark, Kafka, Hadoop, and GPUs.• Collaborating with data science and cross-functional teams to design,build, and deploy data science products in a production environment.