Data Scientist (Economics)

Jibe Ventures; Amsterdam; On-site; EUR 70,000 - 110,000; Full time

Job summary

Qogita is seeking a data scientist with broad quantitative skills and a background in microeconomics, econometrics, or finance. You will own data science work across Qogita's business—from forecasting and classification through experimentation and recommendation systems—and guide pricing decisions in a wholesale marketplace.

Join a cross-functional team with Product, Finance, and Commercial to build the analytical layer that informs how prices are set, how buyers respond, and how market

Qualifications

• 3+ years working as a data scientist or quantitative analyst with exposure to ML methods and statistical modelling focusing 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

Responsibilities

• 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

Skills

Python

SQL

Pricing economics

Econometrics

Education

Bachelor's or Master's in quantitative field

Tools

Pandas

Statsmodels

Scikit-learn

XGBoost

PyTorch

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