Data Scientist

Siri InfoSolutions Inc; Dallas (TX); On-site; USD 96,000 - 179,000; Part time

Job summary

Siri InfoSolutions Inc seeks a Sr Data Scientist for a 18-week engagement in Fort Worth, TX. Onsite 3 days a week (Tue-Thu) with virtual Mondays/Fridays; 3–4 rounds of interviews, including technical/coding tests.

Ideal candidates have 5+ years in quantitative fields, strong Python, Gen AI experience, and production‑grade ML pipelines with deployment and monitoring, plus SQL and data handling skills. Azure ML/Databricks experience is a plus; consulting background helpful.

Qualifications

• Master or PhD with 5+ years in data science or related field.
• Python production-grade coding, modularization, testing and performance tuning.
• Hands-on Gen AI application development including prompt engineering and RAG.
• ML/AI pipeline development, deployment, orchestration, monitoring and optimization.
• Deep knowledge of statistical and machine learning techniques.

Skills

Python proficiency

Gen AI applications

ML pipeline development

SQL

Azure ML

Databricks

ML lifecycle tools

Model deployment

Statistics

Prompt engineering

LLMs

Education

Master or PhD in quantitative discipline

Tools

MLflow

CI/CD

Job description

Title: Sr Data Scientist
Location: Fort Worth, TX, 76155
Duration: 18 Weeks - 08/24/2026 - 12/31/2026

Work Schedule & Interview Process

Candidate must be able to work onsite 3 days a week Tuesday - Thursday; Virtual on Mondays/Fridays

There will be 3-4 round interviews. The first one being a screening, there will be 1-2 technical/coding interviews both virtual, a final in person interview

Job Description:

The technical assessment must demonstrate candidate's skills on Python, Gen AI, and SQL.

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

Experience Required: 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

Nice to Have Skills

• Microsoft Dynamics
• Prompt Engineering
• Proficiency in SQL and working with data
• Experience with ML lifecycle tools (e.g., MLflow, CI/CD, model monitoring)

Ideal Candidate Profile

• Has experience with LLMs and Agentic AI
• Has experience building customer facing Gen AI applications; awareness on guardrails, privacy, cybersecurity concerns
• Has proven experience taking ML models from prototype to production with Python and Azure ML; containerize where needed
• Demonstrated strong skills in code optimization, debugging, and system integration
• Understands end-to-end ML lifecycle, including deployment and monitoring
• Is comfortable working with existing codebases and improving them, as well as building new ones from scratch. Clear communication and effective collaboration