Data Scientist (Economics)
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At a glance
Requirements
Credentials this posting asks for.
Job overview
Qogita is hiring a Data Scientist (Economics). The role seeks a data scientist with broad quantitative skills and a background in microeconomics, econometrics, or finance to own data science work across Qogita's business, including forecasting, classification, experimentation, and recommendation systems, while acting as the team's pricing and market economics expert.
Key focus areas include Own data science work across Qogita's business, from forecasting to recommendation systems, Act as domain expert on pricing and market economics, translating findings into commercial recommendations, and Research, build, deploy and maintain predictive and analytical models reflecting B2B buyer behaviour.
Important skills include Data Science, Microeconomics, Econometrics, Forecasting, Experimentation, and Recommender Systems.
Skills & qualifications
Skills
Qualifications
Full job description
You're a data scientist with broad quantitative skills and a background in microeconomics, econometrics, or finance. You'll own data science work across Qogita's business — from forecasting and classification through to experimentation and recommendation systems — and act as the team's go-to on how prices are set, how buyers respond, and how market structure shapes commercial decisions. The Data Science team works cross-functionally with Product, Finance, and Commercial teams to build the analytical and modelling layer that drives Qogita's wholesale marketplace. Build and deliver data science solutions across the stack — predictive models, segmentation, forecasting, ranking systems, and pricing models — depending on where the business need is greatest
- Act as the team's domain expert on pricing and market economics: take ownership of the modelling approach, analytical strategy, and how findings translate into commercial recommendations
- Research, build, deploy and maintain predictive and analytical models that reflect B2B buyer behaviour and wholesale market dynamics
- Design and analyse experiments and A/B tests, owning statistical validity and translating results into recommendations Product and Commercial can act on
- Apply a range of quantitative methods — regression modelling, causal inference, ML techniques — to business problems across pricing, demand, market liquidity and beyond
- Collaborate with Engineers to ship models via reproducible MLOps workflows, including experiment tracking, model serving, and production monitoring
- Communicate findings and model limitations clearly to Finance, Commercial, and Product stakeholders 3+ years working as a data scientist or quantitative analyst and meaningful exposure across ML methods and statistical modelling with a focus on microeconomics, econometrics or finance
- Strong Python (pandas, statsmodels, scikit-learn, XGBoost, PyTorch) and SQL; comfortable working with large transactional datasets
- Grounding in pricing and market economics — price theory, consumer behaviour, substitution effects, and how market structure shapes pricing power
- Familiarity with econometric techniques for demand estimation, forecasting and measuring causal effects
- Experience designing and analysing experiments with real business decisions riding on the results
- Able to communicate findings and model limitations clearly to non-technical stakeholders across Finance, Commercial, and Product
- Bachelor's or Master's in Data Science, Statistics, Economics, Econometrics, Mathematics, or a related quantitative field
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