What are the responsibilities and job description for the Looking for Data Scientist in Fort Wort TX(Hybrid)- F2F Interview Required position at Saksoft?
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