Data Scientist

Job Summary

We are looking for a Data Scientist with a strong focus on Natural Language Processing (NLP) and applied Large Language Models (LLMs) to support and enhance strategic sourcing and procurement-related processes.

The role emphasizes hands-on development, experimentation and reliance on both SaaS LLMs and locally deployed and hosted open source language models.

You will work closely with sourcing, procurement, and analytics stakeholders to transform unstructured data (contracts, supplier communications, specifications, RFQs, market intelligence, etc.) into actionable insights and scalable tools

Job Description

NLP & LLM Development

1. Design, develop, and deploy NLP pipelines for unstructured text data (e.g. classification, information extraction, summarization, similarity search, entity recognition).
2. Implement prompt engineering, retrieval-augmented generation (RAG), and model evaluation strategies.
3. Optimize models for on-premise or restricted environments, including performance, memory usage, and inference cost.
4. Build and fine-tune open-source / locally hosted LLMs (e.g. LLaMA) for domain-specific use cases.

Strategic Sourcing Use Cases

1. Apply NLP and LLMs to improve sourcing processes such as: Supplier analysis and comparison
2. Contract and clause analysis
3. Contract meta data extraction
4. Spend categorization and enrichment
5. RFQ/RFP creation, analysis and response support
6. Market and supplier risk intelligence
7. Translate business questions into data-driven and AI-powered solutions.

Data Engineering & MLOps

1. Work with structured and unstructured data from multiple internal and external sources.
2. Build reproducible pipelines for data processing, model training, and evaluation.
3. Collaborate on deployment strategies (APIs, batch pipelines, internal tools).
4. Ensure model quality, traceability, and maintainability.

Collaboration & Stakeholder Interaction

1. Partner with procurement, sourcing, and domain experts to deeply understand processes and pain points.
2. Communicate findings, model behavior, and limitations clearly to technical and non-technical audiences.
3. Contribute to defining AI strategy and best practices within the organization.

Required Qualifications

1. Strong background in Data Science, Machine Learning, or Applied AI.
2. Proven experience with NLP techniques (e.g. tokenization, embeddings, transformers, sequence models, LORA/QLORA fine tuning).
3. Hands-on experience with LLMs
4. Strong programming skills in Python and common ML/NLP libraries (e.g. PyTorch, Hugging Face, spaCy).
5. Experience working with unstructured text data at scale.
6. Solid understanding of model evaluation, bias, and limitations.

Nice to Have

1. Experience with procurement, sourcing, supply chain, or contract data.
2. Experience with open-source or self-hosted models.
3. Familiarity with RAG architectures, vector databases, and embedding search.
4. Experience running models in restricted, on-prem, or privacy-sensitive environments.
5. Knowledge of MLOps, model monitoring, and deployment best practices.
6. Experience working closely with business stakeholders in process-driven domains.