Looking for Data Scientist in Fort Wort TX(Hybrid)- F2F Interview Required

Job Title: Data Scientist

Location: Fort Wort TX- Candidate must be able to work onsite 3 days week Tuesday - Thursday; Virtual on Mondays/Fridays.

Contract

In person Interview 

Description:

Minimum Qualifications – Education & Prior Job Experience

• Master or PhD degree with 5+ years of experience in quantitative discipline (e.g., Data Science, Machine Learning Engineer, Computer Science, Applied Mathematics, Statistics, etc.)
• Python proficiency (production-grade coding, modularization, testing, performance tuning)
• Hands on experience with building Gen AI applications – prompt engineering, classifiers, knowledge bases, RAG solutions, LLMs as judges, etc.
• Hands-on experience with ML/AI pipeline development and productionization (model deployment, orchestration, monitoring, and optimization)
• Depth of knowledge in statistical and machine learning techniques

Preferred qualifications – Education & Prior Job Experience

• Experience with Azure ML, Databricks
• Proficiency in SQL and working with data
• Experience with ML lifecycle tools (e.g., MLflow, CI/CD, model monitoring)
• Practical experience designing, building and deploying machine learning models
• 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
• 5-7 years of experience required

Top 3 Mandatory Skills and Experience:

• -Proficient in Python
• -Experience/knowledge on designing and implementing Gen AI applications- Hands-on experience with Machine Learning and AI pipeline build, model deployment, orchestration, monitoring, and optimization