Job Title: Data Scientist - NLP, LLMs & Generative AI
Are you passionate about leveraging AI to solve real-world business challenges? We're looking for a talented Data Scientist to join a growing and forward-thinking data science function focused on driving innovation through NLP, large language models (LLMs), and generative AI.
This is an exciting opportunity to be part of a newly established team, working closely with the Head of Data Science to shape the roadmap, scale AI-driven solutions, and unlock new opportunities across the business. If you're hands-on with Python, experienced in building NLP or LLM-based applications, and thrive in a collaborative, problem-solving environment-this role is for you.

What You'll Do:

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  Design and deploy AI-powered tools that streamline internal processes and drive growth
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  Apply advanced NLP, LLMs, and automation techniques to enhance workflows and insights
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  Identify AI use cases with cross-functional teams and deliver practical, impactful solutions
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  Experiment with emerging open-source LLMs and generative AI technologies for adoption
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  Develop RAG (Retrieval-Augmented Generation) pipelines for extracting insights from unstructured data (PDFs, images, emails, etc.)
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  Support proof-of-concept development and help scale successful solutions across the business

What We're Looking For:
Technical Expertise:

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  5+ years of hands-on experience with Python for machine learning and data science
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  Strong background in ML, NLP, and LLMs, including training/fine-tuning models (e.g., Transformers, encoder-decoder architectures)
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  Experience with open-source LLMs (e.g., LLaMA, Mistral, DeepSeek, Qwen, etc.)
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  Familiarity with Hugging Face Transformers and related NLP libraries
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  Experience building RAG pipelines with embedding models and vector search

Application Development:

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  Comfortable developing APIs using FastAPI or Flask
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  Experience with tools like Streamlit or Gradio for building interactive AI applications
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  Demonstrated ability to integrate LLMs into real-world applications

Infrastructure & Deployment:

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  Experience testing models on local GPU infrastructure
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  Knowledge of Python environment management and containerization (Docker is a plus)

Data & Version Control:

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  Proficient with Git for collaborative development
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  Ability to access and process structured and unstructured data (e.g., SQL, PDFs, images, emails, JSON)