The Role
Own end-to-end data science and machine learning systems, including predictive modeling, forecasting, ranking, and LLM-powered features. Design RAG architectures, evaluate and deploy language models, and maintain reliable production pipelines through MLOps practices. Collaborate with Product, Engineering, and Commercial stakeholders to translate business challenges into measurable ML solutions, while communicating model limitations and trade-offs clearly.
Summary Generated by Built In
You're a data scientist with broad analytical and ML experience as well as production LLM expertise. You'll own the full spectrum of data science work at Qogita — from classical modelling and forecasting through to LLM-powered features — and act as the team's go-to on language model architecture, evaluation, and deployment. You'll take end-to-end ownership of complex ML systems and pipelines that are business-critical: designing them, shipping them, and keeping them healthy in production. The Data Science team works cross-functionally with Product, Engineering, and Commercial teams to build the intelligence layer that drives Qogita's marketplace.
What you'll do
What you'll bring
Benefits
Qogita [Ko-gi-ta] is on a mission to make global trade radically efficient by building the operating system for modern wholesale.
Wholesale is a €50 trillion market still largely run through phone calls, catalogues and trade shows. Behind every product sits a fragmented chain of sourcing, trading and logistics that makes wholesale slower, more complex and expensive than it should be.
Qogita is changing this by connecting the global market into a single order book, building the rails to move products across borders efficiently, and providing the intelligence to help businesses know what to stock, what it’s worth and where it should go.
We operate across health and beauty in 30+ European markets, with ambitions to reach every category, everywhere. Backed by Accel, Bessemer, Dawn and LocalGlobe, we’re one of Europe’s fastest-growing B2B companies.
We’re an ambitious, pragmatic and highly collaborative team, united by a desire to reshape one of the world’s biggest markets.
What you'll do
- Build and deliver data science solutions across the stack — predictive models, ranking systems, demand forecasting, and LLM-powered features — depending on where the business need is greatest
- Take ownership of business-critical ML systems end-to-end: from problem framing and model design through to deployment, monitoring, and ongoing maintenance in production environments
- Act as the team's domain expert on LLMs: advise on model selection, architecture decisions, prompt engineering, fine-tuning, and evaluation
- Design and implement RAG architectures and evaluation frameworks where language models are the right tool for the problem
- Apply classical ML and statistical modelling to structured business problems — pricing signals, supplier matching, catalogue enrichment — with rigorous attention to measurement and validation
- Translate ambiguous business problems into tractable ML problems with clear success criteria, working closely with Product and Commercial stakeholders
- Collaborate with Engineers to ship models via reproducible MLOps workflows — experiment tracking, model serving, alerting, and production monitoring — with a high bar for reliability and observability
- Communicate model choices, limitations, and trade-offs clearly to non-technical stakeholders including Product and commercial leadership
What you'll bring
- 3+ years working as a data scientist or applied ML engineer, with meaningful exposure across both classical ML and deep learning
- A track record of owning ML systems in production — not just building models, but maintaining, monitoring, and iterating on them as live business-critical infrastructure
- Demonstrable LLM expertise — hands-on experience building and evaluating LLM-powered systems in a production or near-production environment
- Solid grounding in ML fundamentals: statistics, probability, supervised and unsupervised learning
- Practical experience with transformer architectures and the major model families (GPT, Claude, Llama, Mistral), including RAG pipeline design and vector database usage
- Strong Python and SQL, with experience using LangChain, XGBoost, PyTorch, Hugging Face Transformers (or similar frameworks), MLOps tooling (experiment tracking, model serving, monitoring), and experience of orchestration for ETL pipelines (Airflow)
- Experience with cloud ML services on AWS, GCP, or Azure, including deploying and operating models in distributed environments
- Able to communicate uncertainty and model limitations clearly to both engineers and non-technical stakeholders
Benefits
- Base salary: €60,000 – €75,000 (Amsterdam) / £72,000 – £90,000 (London) depending on experience
- 26 days of annual leave, plus 4 additional personal days
- Company performance-based bonus
- Attractive equity package
- Pension contributions
- Annual learning & development budget
- Office-led culture with hybrid flexibility
- Dog-friendly offices
- Home-office setup package
- Office socials and annual company-wide offsite
Qogita [Ko-gi-ta] is on a mission to make global trade radically efficient by building the operating system for modern wholesale.
Wholesale is a €50 trillion market still largely run through phone calls, catalogues and trade shows. Behind every product sits a fragmented chain of sourcing, trading and logistics that makes wholesale slower, more complex and expensive than it should be.
Qogita is changing this by connecting the global market into a single order book, building the rails to move products across borders efficiently, and providing the intelligence to help businesses know what to stock, what it’s worth and where it should go.
We operate across health and beauty in 30+ European markets, with ambitions to reach every category, everywhere. Backed by Accel, Bessemer, Dawn and LocalGlobe, we’re one of Europe’s fastest-growing B2B companies.
We’re an ambitious, pragmatic and highly collaborative team, united by a desire to reshape one of the world’s biggest markets.
Skills Required
- 3+ years of experience as a data scientist or applied ML engineer, spanning classical machine learning and deep learning
- Experience owning, maintaining, monitoring, and iterating on production ML systems
- Hands-on experience building and evaluating LLM-powered systems in production or near-production environments
- Strong foundation in statistics, probability, supervised learning, and unsupervised learning
- Practical experience with transformer architectures, GPT, Claude, Llama, Mistral, RAG pipelines, and vector databases
- Strong Python and SQL skills, with experience using LangChain, XGBoost, PyTorch, Hugging Face Transformers, MLOps tooling, and Airflow
- Experience deploying and operating machine learning models using cloud services on AWS, GCP, or Azure in distributed environments
- Ability to communicate uncertainty, model limitations, choices, and trade-offs to engineers and non-technical stakeholders
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The Company
What We Do
Qogita is a leading global wholesale B2B platform that offers a wide variety of products, brands and categories across geographies. We cater to a large range of organizations, from small retailers to large brands. With Qogita, you can generate higher margins and greater turnover whilst reducing labor cost by using our technology infrastructure. We're a technology company that simplifies business-to-business trade. Most recently we have raised €80m in Series B Funding, with our investors being the backers of companies such as Facebook, Pinterest, Linkedin and Twitch.
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