Sr.Data Scientist-Dallas,TX Contract Position C2C

Job Title: Senior Data Scientist
Location: Dallas, TX(Onsite)Duration: Long-Term ContractInterview Mode: Virtual AND Onsite Job Description:8+ years of experience.

Minimum Qualifications Education & Prior Job 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 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: or PhD in Data Science, Computer Science, Statistics, or related field2.Strong Python expertise (production-grade coding, modularization, testing, performance tuning)3.Hands-on experience with Machine Learning pipeline development and productionization (model deployment, orchestration, monitoring, and optimization) Nice to Have Skills and Experience:1. Experience with Databricks / Spark-based ML pipelines2. Proficiency in SQL and working with large-scale datasets3. Experience with ML lifecycle tools (e.g., MLflow, CI/CD, model monitoring)