Sr Data Scientist

Artech LLC

  • Fort Worth, TX
  • 13 days ago

    Highlights

    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.). Hands on experience with building Gen AI applications – prompt engineering, classifiers, knowledge bases, RAG solutions, LLMs as judges, etc.

    Numbers & Facts

    LocationFort Worth, TX

    Description

    Title: Sr Data Scientist
    Location: Fort Worth, TX - 76155
    Duration: 12+ months contract
    Work mode: 3 days hybrid onsite (Tue/Wed/Thu)

    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
    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
    Nice to Have Skills:
    • Client Dynamics
    • Prompt Engineering
    • Proficiency in SQL and working with data
    • Experience with ML lifecycle tools (e.g., MLflow, CI/CD, model monitoring)
    Describe a great candidate that you are looking for and what skills and experience they will have:
    • 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.
    What is the team environment and structure like?:
    • ML/AI team supporting Contact Centers and Digital Customer Experience
    • Will collaborate with several team members
    • Collaborative, supportive, and high-performing culture
    • Strong focus on quality, innovation, scalability, and measurable revenue impact
    How will the resource fit into your team?:
    • The resource will be embedded within the team and support the Service Recovery Modernization initiative.
    • Take ownership of data preparation, data pipelines, design, test, and deployment of ML and Gen AI applications.
    • Collaborate closely with data scientists, data engineers, and product partners.
    • Accelerate delivery of scalable, production-ready AI solutions.

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