Data Scientist

Job Summary:

• Lead and contribute to end-to-end solutions powered by Large Language Models (LLMs).
• Drive innovation and practical applications of LLMs to solve complex business problems.
• Collaborate across teams to deliver scalable, value-driven LLM systems.

Responsibilities

• LLM-Centric Solution Development:
• Design, fine-tune, and implement cutting-edge LLMs for business use cases such as content generation, intelligent assistants, semantic search, or knowledge retrieval.
• Evaluate and select appropriate foundation models (e.g., GPT, LLaMA, Mistral) and tailor them to organization-specific domains.
• Build modular, scalable pipelines for LLM applications, including prompt engineering, inference optimization, and feedback loops.

• Collaborative Project Contribution:
• Work closely with Machine Learning Engineer, Data Engineer, DevOps Engineer, Developers and stakeholders to define project goals and align model capabilities with business outcomes.
• Translate high-level requirements into actionable LLM development plans with measurable KPIs.
• Support the lifecycle of LLM initiatives from experimentation to deployment.

• Technical Excellence:
• Stay current with the rapidly evolving LLM landscape, including open-source models, toolkits (e.g., Hugging Face, LangChain), and retrieval-augmented generation (RAG) frameworks.
• Optimize cost, performance, and latency for LLM inference at scale.
• Ensure reproducibility, quality, and efficiency across LLM workflows.

• Effective Communication & Collaboration:
• Present technical findings and solution designs clearly to both technical and non-technical audiences.
• Share knowledge and contribute to internal LLM best practices and education.
• Offer technical support and informal mentoring on LLM topics across teams.

Requirements:
• Bachelor’s or Master’s in Computer Science, Artificial Intelligence, Data Science, or a related field.
• 2+ years of industry experience in machine learning or data science, including at least 1 year focused on LLM development or deployment.
• Proficient in Python and frameworks relevant to LLM development (e.g., PyTorch, Hugging Face Transformers, LangChain, OpenAI API).
• Practical experience in fine-tuning, evaluating, and integrating LLMs into applications at scale.
• Familiarity with prompt engineering, vector databases (e.g., FAISS), and RAG or LLMOps pipelines.
• Strong analytical and problem-solving skills with the ability to work independently or in cross-functional teams.
• Proficient in English (spoken and written); Japanese skills are a plus but not required.

Benefits:
• Hybrid work
• Health insurance
• Annual health check
• Laptop and other equipments
• Free snacks & drinks
• Education allowance
• Performance bonus