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IT Consulting, Software Development, Staffing

AI/ML Data Scientist

United StatesRemoteFull TimePosted 2 months agoVisa sponsorship available

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Role summary

We are seeking an AI/ML Data Scientist to join our remote team. This role focuses on leveraging statistical techniques, data mining, and advanced programming (Python, R, Scala) to build, validate, and deploy machine learning models end-to-end. You will work with large datasets and data pipelines, translating business problems into analytical frameworks and communicating complex concepts to diverse stakeholders. Experience with Generative AI, LLMs, prompt engineering, fine-tuning, RAG, and cloud AI deployment (Azure, AWS, GCP) is preferred. The ideal candidate possesses strong problem-solving skills and a collaborative mindset to drive impact at scale.

Role: Data Scientist

Location: Remote

FTE Only

Job Description:

  • Familiarity with statistical techniques, data mining, hypothesis testing, and exploratory data analysis
  • Strong knowledge of programming languages with a focus on machine learning and advanced analytics (such as Python, R, Scala)
  • Experience working with large datasets, data pipelines, and relational databases
  • A collaborative mindset with the ability to communicate complex analytical concepts effectively to both technical and non‑technical stakeholders
  • Excellent problem‑solving skills with the ability to analyze issues, identify root causes, and recommend solutions quickly
  • Proven experience building, validating, and deploying machine learning models end‑to‑end in production environments
  • Strong understanding of model performance evaluation, experiment design, and model lifecycle management
  • Ability to translate business problems into analytical frameworks and measurable success metrics
  • Experience working cross‑functionally with engineering, product, and business teams to deliver impact at scale

Good to Have:

  • Experience with Generative AI, including large language models (LLMs), prompt engineering, fine‑tuning, and retrieval‑augmented generation (RAG)
  • Exposure to Agentic AI concepts, such as autonomous agents, tool usage, planning, memory, and multi‑agent workflows
  • Familiarity with modern ML/AI frameworks (e.g., PyTorch, TensorFlow) and MLOps tools for deployment and monitoring
  • Experience deploying AI solutions on cloud platforms (Azure, AWS, or GCP) with an understanding of scalability and cost considerations
  • Knowledge of responsible AI practices, including model explainability, bias mitigation, and governance
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