Sr. Data Scientist C2C contract Dallas, TX Hybrid

Sr. Data Scientist

Location:

Dallas, TX Hybrid

3+ years of experience.

• 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 technique

• 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

• 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

• MS or PhD in Data Science, Computer Science, Statistics, or related field
• Strong Python expertise (production-grade coding, modularization, testing, performance tuning)
• Hands‑on experience with Machine Learning pipeline development and productionization (model deployment, orchestration, monitoring, and optimization)

• Experience with Databricks / Spark-based ML pipelines
• Proficiency in SQL and working with large-scale datasets
• Experience with ML lifecycle tools (e.g., MLflow, CI/CD, model monitoring)

• Has proven experience taking ML models from prototype to production with Python and Databricks.
• Demonstrates strong skills in code optimization, debugging, and system integration.
• Understands the end‑to‑end ML lifecycle, including deployment and monitoring.
• Is comfortable working with existing codebases and improving them, rather than only building new models.
• Clear communication and effective collaboration.