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Qogita is building an operating system for the global wholesale market, which remains largely offline and inefficient. The company connects buyers, sellers, and distributors through a single order book with real-time market intelligence, enabling 95% of transactions to occur without human intervention. The business is doubling annually and operates across 30+ national regimes.
The Data Science team sits at the core of the product, partnering with Product, Engineering, Finance, and Commercial to build the intelligence layer behind pricing, matching, demand forecasting, and product discovery.
In this role, you will own end-to-end data science work across the wholesale marketplace. You'll frame ambiguous commercial problems, build models that ship to production, and maintain them as live infrastructure. Specific responsibilities include:
- Build and deliver data science solutions across the stack: predictive models, ranking, demand forecasting, segmentation, pricing, experimentation, and LLM-powered features
- Take ownership of business-critical ML systems end-to-end: problem framing, model design, deployment, monitoring, and ongoing maintenance
- Translate ambiguous business problems into tractable ML or statistical problems with clear success criteria, working closely with Product and Commercial teams
- Apply quantitative methods (regression, causal inference, classical ML, deep learning) to pricing, demand, liquidity, supplier matching, and catalogue enrichment
- Design and analyse experiments and A/B tests, owning statistical validity and turning results into actionable recommendations
- Collaborate with Engineers to ship models via reproducible MLOps workflows: experiment tracking, model serving, alerting, and production monitoring
- Communicate model choices, limitations, and trade-offs clearly to both technical and non-technical stakeholders
You'll work on hard problems spanning physical goods, multiple regulatory regimes, and increasingly sophisticated buyer agents. The role requires moving fluidly between classical modelling, experimentation, and modern applied ML including LLMs.
REQUIREMENTS:
- 3+ years as a data scientist, applied ML engineer, or quantitative analyst with meaningful exposure across ML methods and statistical modelling
- Track record of owning models in production (not just building them): maintaining, monitoring, and iterating as live infrastructure
- Solid grounding in ML and stats fundamentals: probability, supervised and unsupervised learning, measurement and validation discipline
- Strong Python and SQL; comfortable with large transactional datasets and common DS/ML libraries (pandas, scikit-learn, XGBoost, PyTorch or similar)
- Experience collaborating on MLOps-style workflows (experiment tracking, serving, monitoring) and shipping with engineers
- Ability to communicate uncertainty and model limitations clearly to technical and non-technical audiences
- Bachelor's or Master's in a quantitative field (Data Science, Statistics, Economics, Econometrics, Mathematics, CS, or related), or equivalent experience
NICE TO HAVE:
- Depth in pricing and market economics: price theory, buyer behaviour, demand estimation, causal inference, experimentation with real commercial stakes
- Production LLM systems: RAG, evaluation frameworks, prompt/fine-tuning trade-offs, transformers and major model families, LangChain or similar
- Marketplace or B2B dynamics experience
- AWS/GCP/Azure ML infrastructure
- Tools: Airflow, Docker, dbt, Snowflake, Lightdash or similar
