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Data Scientist

Job in Durham, Durham County, North Carolina, 27703, USA
Listing for: Experis Austria
Full Time position
Listed on 2026-10-13
Job specializations:
  • IT/Tech
    AI Engineer (Applied/Software), Machine Learning/ ML Engineer, Data Scientist
Salary/Wage Range or Industry Benchmark: 60 - 65 USD Hourly USD 60.00 65.00 HOUR
Job Description & How to Apply Below

Experis is hiring an Applied AI Data Scientist to create and evaluate LLM-powered solutions that improve technical support workflows. This is a 100% onsite role in Durham, NC
, focused on practical AI engineering: building assistants, implementing RAG and tool usage, and proving impact with rigorous evaluation. If you enjoy turning support data into reliable AI experiences, you will have a clear path from design through validation and integration.

Compensation for this W2 position is $60 to $65 per hour
. The role centers on systematic experimentation to compare prompts, models, retrieval strategies, and workflow designs, then partner across teams to ship validated solutions.

What you’ll do
  • Design and develop LLM-powered solutions for technical support workflows, including case classification, information retrieval, response drafting, summarization, and agent assistance.
  • Build and enhance AI assistants using prompt engineering
    , tool calling
    , RAG
    , and orchestration techniques.
  • Create evaluation frameworks and datasets to measure accuracy, relevance, groundedness, task completion, latency, and safety
    .
  • Run model evaluations and controlled experiments to compare prompts, models, retrieval strategies, and workflow designs.
  • Work with support teams and subject matter experts to identify business needs, curate knowledge sources, and define success criteria and escalation paths.
  • Analyze structured and unstructured data
    , including support tickets, conversations, and knowledge articles.
  • Monitor AI performance, diagnose failure patterns, and iterate on prompts, retrieval methods, data quality, and safeguards.
  • Apply responsible AI practices
    , including data privacy, security, transparency, human oversight, and bias testing
    .
  • Communicate findings, technical limitations, and recommendations to technical and non-technical stakeholders.
  • Collaborate with software and data engineering teams to integrate validated AI solutions into existing systems.
What you’ll need
  • Hands-on experience with Generative AI
    , LLMs
    , and LLM-powered applications
    .
  • Experience building, testing, or evaluating AI assistants using systematic testing
    , human review
    , error analysis
    , and iterative improvement.
  • Strong understanding of prompt engineering and Retrieval-Augmented Generation (RAG), including embeddings
    , vector search
    , and model selection.
  • Proficiency in Python and SQL for analysis, prototyping, and evaluation.
  • Strong knowledge of statistics
    , experimental design
    , machine learning
    , and model performance measurement.
  • Familiarity with Natural Language Processing (NLP) and text data preparation and analysis.
  • Experience working with AI APIs
    , frameworks, or orchestration tools.
  • Understanding of responsible AI
    , data privacy, security, and governance practices.
Preferred qualifications
  • Experience with technical support, customer service, support tickets, or knowledge management systems.
  • Familiarity with cloud data platforms, data visualization, and production AI monitoring.
  • Experience defining evaluation metrics and improving LLM response quality, reliability, and groundedness.
  • Knowledge of data governance and responsible AI principles.

Location: Durham, NC 27709.
Work arrangement: 100% onsite.

Education & experience
  • Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Mathematics, or a related field, or equivalent practical experience.
  • Minimum 7 years of professional experience in data science, applied machine learning, or a closely related field.
  • Demonstrated experience delivering data science or AI solutions and evaluating performance in real-world applications.
  • Hands-on experience with Generative AI or LLM-based applications, including understanding model limitations and the importance of evaluation and human oversight.

Technologies: Generative AI, LLMs, Retrieval-Augmented Generation (RAG), prompt engineering, embeddings, vector search, Python, SQL, Natural Language Processing (NLP), AI APIs.

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