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September 24, 2026

Data Scientist (Partner Growth & Pricing)

Senior • On-site

Warsaw, Poland

Data Scientist (Partner Growth & Pricing) Quick Facts - Develop and deploy ML models to improve partner growth and pricing competitiveness - Work across ideation to implementation (offline and online models) - Use GenAI/LLMs alongside statistics, predictive modeling, and causal/optimization methods Description You will co-create data science projects that power a trustworthy, competitive marketplace experience. The work focuses on optimizing price competitiveness, expanding product selection, and supporting strategic campaigns through statistical models, rules, and AI—delivered in production at scale. You will collaborate with data engineers and product teams across the full lifecycle, from concept to productization. Responsibilities - Co-create projects from concept to productization to deliver insights and models for business problems - Build and apply a wide range of model types: boosting, Bayesian methods, causal inference, optimization, deep learning, and GenAI/LLM/agentic AI solutions - Implement offline and online models - Work with both tabular and non-tabular data, including spatial data, text, images, and time series - Collaborate with data engineers on data processing pipelines on GCP - Mentor and share knowledge; brainstorm with the team - Work with business stakeholders, analytics, and data engineers Requirements - Experience with data analysis using SQL and building machine learning solutions in production environments, including end-to-end ML model design - Ability to translate business challenges into ML problems - Ability to communicate with business units from problem formulation to clear presentation of results - Experience with ML and AI, especially LLMs - Highly independent, well-organized, and able to take full responsibility for projects - Proficiency in Python and efficient use of development tools - Understanding of statistical and machine learning methods - Practical and theoretical knowledge of Generative AI and Agentic AI principles (coding tools, prompting, validation) - Critical and up-to-date use of GenAI coding tools (e.g., Copilot) - English and Polish at B2+ level - Degree in a strongly related quantitative field (Mathematics, Physics, Economics, Computer Science or similar) or relevant job experience in data science/ML Benefits - Flexible working hours in a hybrid model (4/1), with occasional remote work (up to 30 days) - Annual bonus based on annual performance and company results - Well-located offices and excellent work tools (e.g., ergonomic setup) - Choice of MacBook Pro or equivalent Dell with Windows plus required accessories - Cafeteria benefits plan (e.g., medical, sports, lunch packages, insurance, vouchers) - English classes paid for related to the job - Training budget, inter-team tourism, hackathons, and internal learning platform - Additional day off for volunteering - Social events and community activities - Access to modern AI tools and a culture of autonomy, knowledge sharing, and engineering best practices formatted_html_description

Quick Facts

  • Develop and deploy ML models to improve partner growth and pricing competitiveness
  • Work across the full lifecycle (offline and online models)
  • Use GenAI/LLMs alongside statistics, predictive modeling, and causal/optimization methods

Description

You will co-create data science projects that power a trustworthy, competitive marketplace experience. The work focuses on optimizing price competitiveness, expanding product selection, and supporting strategic campaigns through statistical models, rules, and AI—delivered in production at scale. You will collaborate with data engineers and product teams across the full lifecycle, from concept to productization.

Responsibilities

  • Co-create projects from concept to productization to deliver insights and models for business problems
  • Build and apply a wide range of model types: boosting, Bayesian methods, causal inference, optimization, deep learning, and GenAI/LLM/agentic AI solutions
  • Implement offline and online models
  • Work with both tabular and non-tabular data, including spatial data, text, images, and time series
  • Collaborate with data engineers on data processing pipelines on GCP
  • Mentor and share knowledge; brainstorm with the team
  • Work with business stakeholders, analytics, and data engineers

Requirements

  • Experience with data analysis using SQL and building machine learning solutions in production environments, including end-to-end ML model design
  • Ability to translate business challenges into ML problems
  • Ability to communicate with business units from problem formulation to clear presentation of results
  • Experience with ML and AI, especially LLMs
  • Highly independent, well-organized, and able to take full responsibility for projects
  • Proficiency in Python and efficient use of development tools
  • Understanding of statistical and machine learning methods
  • Practical and theoretical knowledge of Generative AI and Agentic AI principles (coding tools, prompting, validation)
  • Critical and up-to-date use of GenAI coding tools (e.g., Copilot)
  • English and Polish at B2+ level
  • Degree in a strongly related quantitative field (Mathematics, Physics, Economics, Computer Science or similar) or relevant job experience in data science/ML

Benefits

  • Flexible working hours in a hybrid model (4/1), with occasional remote work (up to 30 days)
  • Annual bonus based on annual performance and company results
  • Well-located offices and excellent work tools (e.g., ergonomic setup)
  • Choice of MacBook Pro or equivalent Dell with Windows plus required accessories
  • Cafeteria benefits plan (e.g., medical, sports, lunch packages, insurance, vouchers)
  • English classes paid for related to the job
  • Training budget, inter-team tourism, hackathons, and internal learning platform
  • Additional day off for volunteering
  • Social events and community activities
  • Access to modern AI tools and a culture of autonomy, knowledge sharing, and engineering best practices

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