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Senior Data Scientist GenAI / RAG

  • Texas, Houston

  • 07/02/2026

  • Full Time

  • Active

Job Description:

  • Job Summary
    We are seeking a Senior Data Scientist with a strong background in traditional Machine Learning, Deep Learning, and hands-on experience building enterprise Generative AI solutions. This role is responsible for developing and deploying AI-powered applications, Retrieval-Augmented Generation (RAG) systems, predictive models, and data-driven solutions that solve complex business problems. The ideal candidate will collaborate with cross-functional teams, interact directly with customers, and leverage modern AI technologies to deliver scalable enterprise solutions.

    Key Responsibilities
    • Build, deploy, and optimize Retrieval-Augmented Generation (RAG) systems and AI-powered chat interfaces.
    • Develop enterprise Generative AI solutions using Large Language Models (LLMs) and related technologies.
    • Design, develop, and implement machine learning algorithms to solve complex business problems.
    • Analyze large and complex datasets to generate actionable insights and support data-driven decision-making.
    • Build predictive models and statistical solutions to improve enterprise products and business outcomes.
    • Collaborate with client data science teams across Machine Learning and Deep Learning ecosystems.
    • Work closely with product managers, engineers, and business stakeholders to integrate AI and data science solutions into enterprise products.
    • Develop and deploy machine learning models for production environments.
    • Perform feature engineering, data preparation, and model optimization.
    • Build enterprise knowledge solutions using Generative AI technologies.
    • Work with structured and unstructured datasets to extract meaningful insights.
    • Collaborate directly with customers and stakeholders to understand business requirements and recommend AI-driven solutions.
    • Communicate technical findings and recommendations effectively to both technical and non-technical audiences.
    • Mentor junior data scientists and contribute to knowledge sharing across the team.
    • Stay current with advancements in Machine Learning, Deep Learning, Generative AI, and data science technologies.
    • Support continuous improvement initiatives by evaluating emerging AI frameworks, tools, and best practices.

    Required Qualifications
    • Overall 10+ years of professional experience in Data Science, Machine Learning, Artificial Intelligence, or related technical fields.
    • Strong experience in Machine Learning (ML) and Deep Learning (DL).
    • Hands-on experience with Generative AI technologies, including Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) systems.
    • Experience developing enterprise AI and machine learning solutions.
    • Strong proficiency in Python or R.
    • Experience with machine learning frameworks such as TensorFlow, PyTorch, or Scikit-learn.
    • Strong understanding of statistical analysis, predictive modeling, and data modeling techniques.
    • Experience working with SQL and querying large datasets.
    • Familiarity with big data technologies such as Hadoop, Spark, or similar platforms.
    • Experience working within AWS cloud environments.
    • Experience working with Snowflake or similar enterprise data platforms.
    • Strong analytical, problem-solving, and critical-thinking skills.
    • Excellent communication, collaboration, stakeholder management, and client-facing skills.
    • Ability to work effectively with cross-functional teams in an enterprise environment.

    Preferred Qualifications
    • Exposure to Agentic AI and Agent APIs.
    • Experience with Claude, Anthropic, or similar Generative AI development platforms.
    • Domain experience within the Oil & Gas or Energy industry.
    • Master's or Ph.D. in Computer Science, Statistics, Mathematics, or a related quantitative field.
    • Experience working with enterprise software products.
    • Understanding of cloud platforms such as AWS, Microsoft Azure, or Google Cloud Platform.

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