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About the Role
Own the full spectrum of data science work at Qogita, from classical modeling and forecasting through to LLM-powered features. Act as the team's go-to on language model architecture, evaluation, and deployment. Take end-to-end ownership of complex, business-critical ML systems and pipelines.
Responsibilities
- Build and deliver data science solutions across the stack, including predictive models, ranking systems, demand forecasting, and LLM-powered features.
- Take ownership of business-critical ML systems end-to-end, from problem framing and model design through to deployment, monitoring, and maintenance.
- Act as the team's domain expert on LLMs, advising on model selection, architecture, prompt engineering, fine-tuning, and evaluation.
- Design and implement RAG architectures and evaluation frameworks.
- Apply classical ML and statistical modeling to structured business problems with rigorous attention to measurement and validation.
- Translate ambiguous business problems into tractable ML problems with clear success criteria, collaborating with Product and Commercial stakeholders.
- Collaborate with Engineers to ship models via reproducible MLOps workflows, ensuring reliability and observability.
- Communicate model choices, limitations, and trade-offs clearly to non-technical stakeholders.
Requirements
- 3+ years of experience as a data scientist or applied ML engineer with exposure to both classical ML and deep learning.
- Proven track record of owning ML systems in production, including maintenance, monitoring, and iteration.
- Demonstrable LLM expertise with hands-on experience building and evaluating LLM-powered systems.
- Solid grounding in ML fundamentals: statistics, probability, supervised and unsupervised learning.
- Practical experience with transformer architectures and major model families (GPT, Claude, Llama, Mistral), including RAG pipeline design and vector database usage.
- Strong Python and SQL skills.
- Experience using LangChain, XGBoost, PyTorch, Hugging Face Transformers (or similar frameworks).
- Experience with MLOps tooling (experiment tracking, model serving, monitoring).
- Experience with orchestration for ETL pipelines (Airflow).
- Experience with cloud ML services on AWS, GCP, or Azure, including deploying and operating models in distributed environments.
- Ability to communicate uncertainty and model limitations clearly to both technical and non-technical stakeholders.
Skills
- Machine Learning
- Deep Learning
- LLMs
- Language Model Architecture
- Model Evaluation
- Model Deployment
- RAG Architectures
- Classical ML
- Statistical Modeling
- Prompt Engineering
- Fine-tuning
- Transformer Architectures
- GPT
- Claude
- Llama
- Mistral
- Vector Databases
- Python
- SQL
- LangChain
- XGBoost
- PyTorch
- Hugging Face Transformers
- MLOps
- Experiment Tracking
- Model Serving
- Production Monitoring
- ETL Pipelines
- Airflow
- AWS
- GCP
- Azure
Location
- Amsterdam
- London
Work Type
- Hybrid
Experience Level
- 3+ years
Salary/Compensations
- €60,000 – €75,000 (Amsterdam)
- £72,000 – £90,000 (London)
Benefits
- 26 days of annual leave
- 4 additional personal days
- Company performance-based bonus
- Attractive equity package
- Pension contributions
- Annual learning & development budget
- Hybrid flexibility
- Dog-friendly offices
- Home-office setup package
- Office socials and annual company-wide offsite
About the Company
- Qogita is revolutionizing wholesale procurement with a one-stop shop for branded products.
- Our vision is to build the world's leading global wholesale trading hub.
- We are one of the fastest-growing B2B companies globally, backed by top investors.
- Our team thrives on curiosity and impact, valuing a strong work ethic, smart prioritization, and a relentless focus on excellence.